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Association Rules or Market Basket Analysis with R - An Example
 
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Provides an example of steps involved in carrying out association rule analysis in R. Association rule analysis is also called market basket analysis or affinity analysis. Some examples of companies using this method include Amazon, Netflix, Ford, etc. Definitions for support, confidence and lift are also included. Also includes, - use of rules package and a priori function - reducing number of rules to manageable size by specifying parameter values - finding interesting and useful rules - finding and removing redundant rules - sorting rules by lift - visualizing rules using scatter plot, bubble plot and graphs R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 18910 Bharatendra Rai
Graph Mining: Laws, Generators & Tools
 
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Prof. Christos Faloutsos Carnegie Mellon University October 15, 2007 -_-_-_-_-_-_-_-_-_-_-_- Samuel D. Conte Distinguished Lecture Series in Computer Science Sponsored by the Purdue University Department of Computer Science
Views: 2687 Purdue University
Final Year Projects | Mining Frequent Subgraph Patterns from Uncertain Graph Data
 
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Final Year Projects | Mining Frequent Subgraph Patterns from Uncertain Graph Data More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1628 Clickmyproject
Spectral algorithms for graph mining and analysis Yiannis Koutis
 
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Spectral algorithms have long been recognized as a signifi-cant tool in the analysis and mining of large graphs. How-ever, their adoption remains relatively limited because they are perceived as computationally demanding or non-robust. The talk addresses these two issues. We review recent algo-rithmic progress that enables the very fast computation of graph eigenvectors in time nearly linear to the size of the graph, making them very appealing from a computational point of view. We also review theoretical results that pro-vide strong arguments in favor of spectral algorithms from a robustness point of view, showing that Cheeger inequal-ities are rather pessimistic for significant classes of graphs that include real-world networks. We further argue that we have only scratched the surface in understanding the power of spectral methods for graph analysis. We support this claim by discussing non-standard “generalized” graph eigen-vectors, and showing that minor modifications of the default spectral partitioning methods have the potential to enhance their efficacy.
Views: 793 MMDS Foundation
Data Science & Machine Learning - Apriori Hands-on Example - DIY- 37 -of-50
 
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Data Science & Machine Learning - Apriori Hands-on Example - DIY- 37 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Hands On – R Machine Learning Ex-17 Get the Titanic: Machine Learning from Disaster data set from the following link, and predict survival on the Titanic passengers using Apriori Algorithm. https://www.kaggle.com/c/titanic/data Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Data Science & Machine Learning - Random Forest Intro - DIY- 28 -of-50 Data Science & Machine Learning - Random Forest Hands on - DIY- 29 -of-50 Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50 Data Science & Machine Learning - Naive Bayes Handson contd- DIY- 33 -of-50 Data Science & Machine Learning - Naive Bayes Exercise- DIY- 34 -of-50 Data Science & Machine Learning - Apriori Algorithm Concepts- DIY- 35 -of-50 Data Science & Machine Learning - Support Confidence Lift - Apriori- DIY- 36 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori
Graph Mining Algorithm
 
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Graph Mining Algorithm for temporal dependency discovery developed by INSA Lyons funded by FP7-PEOPLE-2013-IAPP Industry Academia Partnerships and Pathways ID 612334 (2014-2018)
Views: 129 FP7 Graisearch
Topic Model for Graph Mining | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 93 Clickmyproject
Partitioning
 
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Views: 15281 VLSI Physical Design
Apriori Algorithm ll Generating Association Rules Explained With Example in Hindi
 
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Apriori Algorithm Part-1 https://youtu.be/WCK09hVXI9M Apriori Algorithm Explained With Solved Example Generating Association Rules. Association Rules Are Primary Aim or Output Of Apriori Algorithm. 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 16072 5 Minutes Engineering
Mining Health Examination Records A Graph based Approach
 
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General health examination is an integral part of healthcare in many countries. Identifying the participants at risk is important for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk prediction lies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes the participants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth for differentiating their states of health. In this paper, we propose a graph-based, semi-supervised learning algorithm called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method
Views: 195 Sagar jate
apriori algorithm in WEKA
 
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This tutorial is about how to apply apriori algorithm on given data set. This is association rule mining task. #datamining #weka #apriori Data mining in hindi Data mining tutorial Weka tutorial
Views: 2600 yaachana bhawsar
Common Sub-graph Mining and Visualization
 
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2014 Final Project @Trend Micro To make virus examiners efficiently figure out which behaviors are the features in each cluster, I applied Likelihood Ratio (a familiar testing method for information retrieval) for feature selection. Furthermore, to help them understand the relationship between each behavior, I used Vector Space Model to find sub-graph of each cluster of virus.
Views: 624 cytms
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 69706 WekaMOOC
Apriori Algorithm with R Studio
 
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This is a video for RMD Sinhgad School of Engineering (BE-Computer) as a demonstration for one of the assignments of Business Analytics and Intelligence. Important Links: Ubuntu 16.04.2 LTS Download: https://www.ubuntu.com/download/desktop R installation instructions: https://www.datascienceriot.com/how-to-install-r-in-linux-ubuntu-16-04-xenial-xerus/kris/ R studio Download: https://www.rstudio.com/products/rstudio/download/ R Tutorial: http://tryr.codeschool.com/
Views: 7568 Varun Joshi
Facebook Friend Recommendation using Graph Mining @Applied AI Course/ AI Case Study
 
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for more details please visit this link https://www.appliedaicourse.com/courses/facebook-friend-recommedation
Views: 3118 Applied AI Course
Frequent Pattern Mining - Apriori Algorithm
 
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Here's a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern University, Boston.
Views: 70066 djitz
R - Association Rules - Market Basket Analysis (part 1)
 
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Association Rules for Market Basket Analysis using arules package in R. The data set can be load from within R once you have installed and loaded the arules package. Association Rules are an Unsupervised Learning technique used to discover interesting patterns in big data that is usually unstructured as well.
Views: 54573 Jalayer Academy
FloCon 2015: Graph Based Role Mining Techniques for Cyber Security by Oler and Choudhury
 
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Watch Kiri Oler and Sutanay Choudhury of Pacific Northwest Laboratory discuss Graph Based Role Mining Techniques for Cyber Security.
RMonto: frequent pattern discovery tutorial
 
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New version: http://youtu.be/-6kygKoN7Go
Views: 649 RMontoExtension
Spectral Orange: Introduction
 
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Introduction to Spectral Orange, a flavor of Orange for analyzing spectroscopy data. Please note that Collagen spectroscopy data set has been renamed to Liver spectroscopy. Full Quasar package: https://quasar.codes/ Get Orange: https://orange.biolab.si/ See Spectroscopy add-on: https://github.com/markotoplak/orange-infrared License: GNU GPL + CC Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana In collaboration with: Soleil Synchrotron, Elettra Sincrotrone Trieste, BioSpec Norway and Canadian Light Source. Design: Agnieszka Rovšnik Music: THE HAPPY SONG by Nicolai Heidlas Music https://soundcloud.com/nicolai-heidlas Creative Commons — Attribution 3.0 Unported— CC BY 3.0 http://creativecommons.org/licenses/b... Music promoted by Audio Library https://youtu.be/cGuaRsXLScQ
Views: 5135 Orange Data Mining
The Apriori Algorithm ... How The Apriori Algorithm Works
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 163386 Noureddin Sadawi
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134786 nptelhrd
Data Mining with RapidMiner - LinearRegression (Thai)
 
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This video is about Datamining-LinearRegression
Views: 361 Damrongsak Naparat
Week 7: Text Mining Conceptual Overview of Techniques
 
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Carolyn Rose discusses text mining conceptual overview of techniques for week 7 of DALMOOC.
How can graph analytics help my business?
 
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Introduction to graph analytics and how Oracle Big Data Spatial and Graph can solve numerous problems across different industries. Read more about Big Data Spatial and Graph: 1. Oracle Big Data Spatial and Graph on Oracle.com:  https://www.oracle.com/database/big-data-spatial-and-graph 2. OTN product page (trial software downloads, documentation):  http://www.oracle.com/technetwork/database/database-technologies/bigdata-spatialandgraph 3. Blog  (technical examples and tips):  https://blogs.oracle.com/bigdataspatialgraph/ 4. Big Data Lite Virtual Machine (a free sandbox environment to get started):   http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html
Ben Chamberlain - Real time association mining in large social networks
 
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PyData London 2016 Social media can be used to perceive the relationships between individuals, companies and brands. Understanding the relationships between key entities is of vital importance for decision support in a swathe of industries. We present a real-time method to query and visualise regions of networks that could represent an industries, sports or political parties etc. There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. We present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in Python based software and uses the scipy stack (specifically numpy, pandas, scikit-learn and matplotlib) as well as the python igraph implementation. Slides available here: https://docs.google.com/presentation/d/1-NkcPM3XYn-7jk6233MvvFJiC5Abi3e2nGkF_NSFuFA/edit?usp=sharing Additional information: http://krondo.com/in-which-we-begin-at-the-beginning/
Views: 748 PyData
VC-Dimension and Rademacher Averages - Part 1
 
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Author: Matteo Riondato, Eli Upfal Abstract: Rademacher Averages and the Vapnik-Chervonenkis dimension are fundamental concepts from statistical learning theory. They allow to study simultaneous deviation bounds of empirical averages from their expectations for classes of functions, by considering properties of the functions, of their domain (the dataset), and of the sampling process. In this tutorial, we survey the use of Rademacher Averages and the VC-dimension in sampling-based algorithms for graph analysis and pattern mining. We start from their theoretical foundations at the core of machine learning, then show a generic recipe for formulating data mining problems in a way that allows to use these concepts in efficient randomized algorithms for those problems. Finally, we show examples of the application of the recipe to graph problems (connectivity, shortest paths, betweenness centrality) and pattern mining. Our goal is to expose the usefulness of these techniques for the data mining researcher, and to encourage research in the area. ACM DL: http://dl.acm.org/citation.cfm?id=2789984 DOI: http://dx.doi.org/10.1145/2783258.2789984
First time Weka Use : How to create & load data set in Weka : Weka Tutorial # 2
 
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This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Views: 41545 HowTo
TutORial: Machine Learning and Data Mining with Combinatorial Optimization Algorithms
 
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By Dorit Simona Hochbaum. The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.
Views: 141 INFORMS
Part 1.3 | Olap vs Oltp in hindi | online analytical processing online transaction processing
 
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• Counselling Guruji is our latest product & a well-structured program that answers all your queries related to Career/GATE/NET/PSU’s/Private Sector etc. You can register for the program at: https://goo.gl/forms/ZmLB2XwoCIKppDh92 You can check out the brochure at: https://www.google.com/url?q=http://www.knowledgegate.in/guruji/counselling_guruji_brochure.pdf&sa=D&ust=1553069285684000&usg=AFQjCNFaTk4Pnid0XYyZoDTlAtDPUGcxNA • Link for the complete playlist of DBMS is: https://www.youtube.com/playlist?list=PLmXKhU9FNesR1rSES7oLdJaNFgmuj0SYV • Links for the books that we recommend for DBMS are: 1.Database System Concepts (Writer: Avi Silberschatz · Henry F.Korth · S. Sudarshan) (Publisher: McGraw Hill Education) https://amzn.to/2HoR6ta 2.Fundamentals of database systems (Writer:Ramez Elmsari,Shamkant B.Navathe) https://amzn.to/2EYEUh2 3.Database Management Systems (Writer: Raghu Ramkrishnan, JohannesGehrke) https://amzn.to/2EZGYph 4.Introduction to Database Management (Writer: Mark L. Gillenson, Paulraj Ponniah, Alex Kriegel, Boris M. Trukhnov, Allen G. Taylor, and Gavin Powell with Frank Miller.(Publisher: Wiley Pathways) https://amzn.to/2F0e20w • Check out our website http://www.knowledgegate.in/ • Please spare some time and fill this form so that we can know about you and what you think about us: https://goo.gl/forms/b5ffxRyEAsaoUatx2 • Your review/recommendation and some words can help validating our quality of content and work so Please do the following: - 1) Give us a 5-star review with comment on Google https://goo.gl/maps/sLgzMX5oUZ82 2) Follow our Facebook page and give us a 5-star review with comments https://www.facebook.com/pg/knowledgegate.in/reviews 3) Follow us on Instagram https://www.instagram.com/mail.knowledgegate/ 4) Follow us on Quora https://www.quora.com/profile/Sanchit-Jain-307 • Links for Hindi playlists of other Subjects are: TOC: https://www.youtube.com/playlist?list=PLmXKhU9FNesSdCsn6YQqu9DmXRMsYdZ2T OS: https://www.youtube.com/playlist?list=PLmXKhU9FNesSFvj6gASuWmQd23Ul5omtD Digital Electronics: https://www.youtube.com/playlist?list=PLmXKhU9FNesSfX1PVt4VGm-wbIKfemUWK Discrete Mathematics: Relations:https://www.youtube.com/playlist?list=PLmXKhU9FNesTpQNP_OpXN7WaPwGx7NWsq Graph Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesS7GpOddHDX3ZCl86_cwcIn Group Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesQrSgLxm6zx3XxH_M_8n3LA Proposition:https://www.youtube.com/playlist?list=PLmXKhU9FNesQxcibunbD82NTQMBKVUO1S Set Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesTSqP8hWDncxpCj8a4uzmu7 Data Structure: https://www.youtube.com/playlist?list=PLmXKhU9FNesRRy20Hjr2GuQ7Y6wevfsc5 Computer Networks: https://www.youtube.com/playlist?list=PLmXKhU9FNesSjFbXSZGF8JF_4LVwwofCd Algorithm: https://www.youtube.com/playlist?list=PLmXKhU9FNesQJ3rpOAFE6RTm-2u2diwKn • About this video: This video discuss two types of database OLTP and OLAP. What is online transaction processing and what is online analytical processing. Properties of OLTP, Properties on OLAP, type of data in olap, type of data in oltp, what is historical data, where OLTP is used, where OLAP is used, Why we need OLTP and OLAP, Difference between OLTP and OLAP in dbms is discussed. OLAP features: i)stores historical data ii)It is subject oriented iii) It is useful in decision making iv)Used by CEO’s, General managers, high officials of company OLTP features: i)stores current data ii) It is application oriented iii)It is useful for day to day operations iv)Used by clerks, managers and employees of company database tutorial in hindi, definition of data in dbms, components of dbms in hindi,difference between oltp and olap, types of data in dbms dbms tutorials for gate, dbms for beginners in hindi, 3-tier architecture of dbms in hindi,dbms for net,knowledge gate dbms,advantage of dbms, disadvantage of file in dbms, DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,Ordered Indexing,Select operation,Join operations, Natural joins, SQL commands,File structure in DBMS,Primary Indexing,Clustered Indexing,Concurrency control protocols,
Views: 76443 KNOWLEDGE GATE
Final Year Projects | An efficient tree-based algorithm for mining sequential patterns with multiple
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 499 Clickmyproject
Getting Started with Orange 04: Loading Your Data
 
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Loading your data in Orange from Google sheets or Excel. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 64394 Orange Data Mining
Another Market Basket Analysis in Tableau
 
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In this version we’ll use default Tableau Superstore data to show the relationship between sub-categories on an Order; all without using a self table join. The visualization and analysis is driven by a user selection parameter. This video represents part two in my Market Basket Analysis series. Website: anthonysmoak.com Twitter: @AnthonySmoak
Views: 997 Anthony B. Smoak
Frequent Itemset Mining for Big Data
 
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Frequent Itemset Mining for Big Data Data Alcott Systems 09600095046 [email protected]
Views: 648 finalsemprojects
Business Analytics with Excel | Data Science Tutorial | Simplilearn
 
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Business Analytics with excel training has been designed to help initiate you to the world of analytics. For this we use the most commonly used analytics tool i.e. Microsoft Excel. The training will equip you with all the concepts and hard skills required to kick start your analytics career. If you already have some experience in the IT or any core industry, this course will quickly teach you how to understand data and take data driven decisions relative to your domain using Microsoft excel. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Excel-W3vrMSah3rc&utm_medium=SC&utm_source=youtube For a new-comer to the analytics field, this course provides the best required foundation. The training also delves into statistical concepts which are important to derive the best insights from available data and to present the same using executive level dashboards. Finally we introduce Power BI, which is the latest and the best tool provided by Microsoft for analytics and data visualization. What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 34196 Simplilearn
IEEE 2016: FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce
 
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We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website Note: Voice Video Listen with audio Visit : www.javafirst.in Contact: 73383 45250
Hashing and Hash table in data structure and algorithm
 
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This video lecture is produced by S. Saurabh. He is B.Tech from IIT and MS from USA. hashing in data structure hash table hash function hashing in dbms To study interview questions on Linked List watch http://www.youtube.com/playlist?list=PL3D11462114F778D7&feature=view_all To prepare for programming Interview Questions on Binary Trees http://www.youtube.com/playlist?list=PLC3855D81E15BC990&feature=view_all To study programming Interview questions on Stack, Queues, Arrays visit http://www.youtube.com/playlist?list=PL65BCEDD6788C3F27&feature=view_all To watch all Programming Interview Questions visit http://www.youtube.com/playlist?list=PLD629C50E1A85BF84&feature=view_all To learn about Pointers in C visit http://www.youtube.com/playlist?list=PLC68607ACFA43C084&feature=view_all To learn C programming from IITian S.Saurabh visit http://www.youtube.com/playlist?list=PL3C47C530C457BACD&feature=view_all
Views: 327238 saurabhschool
Market Basket Analysis in Tableau
 
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One of retailers’ favorite analysis techniques to help them understand the purchase behavior of their customers is the market basket analysis. We'll use Tableau to perform a simple market basket analysis based upon default Superstore data. anthonysmoak.com @anthonysmoak
Views: 2698 Anthony B. Smoak
How to create line Chart or Graph in Excel ( Scatter chart ) | In bangla
 
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How to Use a scatter chart (XY chart) to show scientific XY data. Scatter charts are often used to find out if there's a relationship between two variable X & Y. How to create line Chart or Graph in Excel ( Scatter chart ) | In bangla. Sc
Views: 252 HASAN ACADEMY
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 467 The Audiopedia
Data Mining & Business Intelligence | Tutorial #26 | OPTICS
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #OPTICS Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj OPTICS is a density based clustering technique in data mining for identifying arbitrary shaped clusters. Watch Now ! OPTICS هي تقنية تجميع تعتمد على الكثافة في التنقيب عن البيانات لتحديد المجموعات العشوائية. شاهد الآن ! ОПТИКА - это метод кластеризации на основе плотности при добыче данных для идентификации кластеров произвольной формы. Смотри ! OPTICS es una técnica de agrupación basada en la densidad en la minería de datos para identificar clusters con formas arbitrarias. Ver ahora ! OPTICS ist eine dichte-basierte Clustering-Technik im Data Mining zur Identifizierung beliebig geformter Cluster. Schau jetzt ! OPTICS est une technique de clustering basée sur la densité dans l'exploration de données pour identifier des groupes de formes arbitraires. Regarde maintenant ! OPTICS é uma técnica de clustering baseada em densidade em mineração de dados para identificar clusters de forma arbitrária. Assista agora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 1943 Ranji Raj
Incremental Subspace Data-Mining Algorithm Based on Data-flow Density of Complex Networks
 
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Lijuan L. Incremental Subspace Data-Mining Algorithm Based on Data-flow Density of Complex Networks. Journal of Networks, 2014. 9(11): 3175-3180 Shazmeen SF, Baig M M A, Pawar M R. Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis. Journal of Computer Engineering, 2013, 10(6): 01-06. Chen Y G. On-line fast kernel based methods for classification over stream data (with case studies for cyber-security). Auckland University of Technology. 2012.
Views: 177 Leilani Lotti
Type Of Data Visualization ll Line Chart,Area Chart, Pie Chart and Flowchart Explained in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 4880 5 Minutes Engineering
Data Mining - Facebook part 1
 
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This tutorial shows how to access,use and communicate with Facebook API using graph API explorer.It gives a brief idea about what kind of data we can retrieve from Facebook.
Views: 27268 Vikash Khairwal
Identifying product opportunities using social media mining: Application of topic modeling
 
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Identifying product opportunities using social media mining: Application of topic modeling and chance discovery theory - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project BIG DATA 1. A Meta Path based Method for Entity Set Expansion in Knowledge Graph 2. Towards Green Cloud Computing: Demand Allocation and Pricing Policies for Cloud Service Brokerage 3. Security-Aware Resource Allocation for Mobile Social Big Data: A Matching Coalitional Game Solution 4. Revocable Identity-Based Access Control for Big Data with Verifiable Outsourced Computing 5. An Efficient and Fine-Grained Big Data Access Control Scheme With Privacy-Preserving Policy 6. HDM:A Compostable Framework for Big Data Processing 7. Dip-SVM : Distribution Preserving KernelSupport Vector Machine for Big Data 8. A Secure and Verifiable Access Control Scheme for Big Data Storage in Cloud 9. Game Theory Based Correlated Privacy Preserving Analysis in Big Data 10. Secure Authentication in Cloud Big Data with Hierarchical Attribute Authorization Structure 11. System to Recommend the Best Place to Live Based on Wellness State of the User Employing 12. Efficient Top-k Dominating Computation on Massive Data 13. Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing 14. Disease Prediction by Machine Learning over Big Data from Healthcare Communities 15. Machine Learning with Big Data: Challenges and Approaches 16. Analyzing Healthcare Big Data with Predictionfor Future Health Condition 17. Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid 18. iShuffle: Improving Hadoop Performance with Shuffle-on-Write 19. Optimizing Share Size in Efficient and Robust Secret Sharing Scheme 20. Big data privacy in Biomedical research 21. Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications 22. STaRS: Simulating Taxi Ride Sharing at Scale 23. Modeling Urban Behavior by Mining Geotagged Social Data 24. Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data 25. Managing Big data using Hadoop Map Reduce in Telecom Domain 26. A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility with Pairing-Based Cryptography 27. Mutual Privacy Preservingk-Means Clustering in Social Participatory Sensing 28. Measuring Scale-Up and Scale-Out Hadoop with Remote and Local File Systems and Selecting the Best Platform 29. Efficient Recommendation of De-identification Policies using MapReduce CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search
Analysis of Chicago City Crime Data Using Data Mining CS 5593 OU
 
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Application for the project of Analysis of Chicago City Crime Data using Data mining for The University of Oklahoma class CS - 5593 0:00 Clustering application 5:37 Classification Application Members of the group: Cristian Paez Pravallika Uppuganti Ryan Kiel
Views: 1133 Cristian Paez

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