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Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 259845 Well Academy
Association Rule Mining Project in Java PPT
 
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Download Association Rule Mining Project in Java PPT Source code in java, project report, documentation, ppt for free download. http://freeprojectscode.com/java-projects/association-rule-mining/1237/ http://1000projects.org/efficient-association-rule-mining-algorithm-distribute-project-java.html
Views: 1049 kasarla shashank
Final Year Projects | An IntrusionDetection Model Based on Fuzzy Class-Association-Rule Mining Using
 
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Final Year Projects | An IntrusionDetection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Progr 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: 625 Clickmyproject
Secure Mining of Association Rules in  Horizontally Distributed Databases
 
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Secure Mining of Association Rules in Horizontally Distributed Databases LeMeniz Infotech A Leading Software Concern Stepping in IEEE Projects 2014-2015. Do Your Projects With Domain Experts. To Get this Projects with Complete Document Call Us Rafee 9962588976 / 9566355386 Web : http://www.lemenizinfotech.com/tag/online-ieee-projects/ blog : http://www.lemenizinfotech.blogspot.in blog : http://www.ieeeprojectsinpondicherry.blogspot.in Download App from http://www.ieeeprojectspondicherry.weebly.com
Views: 120 LeMeniz Infotech
Locally Differentially Private Frequent Itemset Mining
 
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Locally Differentially Private Frequent Itemset Mining Tianhao Wang (Purdue University) Presented at the 2018 IEEE Symposium on Security & Privacy May 21–23, 2018 San Francisco, CA http://www.ieee-security.org/TC/SP2018/ ABSTRACT The notion of Local Differential Privacy (LDP) enables users to respond to sensitive questions while preserving their privacy. The basic LDP frequent oracle (FO) protocol enables an aggregator to estimate the frequency of any value. But when each user has a set of values, one needs an additional padding and sampling step to find the frequent values and estimate their frequencies. In this paper, we formally define such padding and sample based frequency oracles (PSFO). We further identify the privacy amplification property in PSFO. As a result, we propose SVIM, a protocol for finding frequent items in the set-valued LDP setting. Experiments show that under the same privacy guarantee and computational cost, SVIM significantly improves over existing methods. With SVIM to find frequent items, we propose SVSM to effectively find frequent itemsets, which to our knowledge has not been done before in the LDP setting.
Optimized Association Rule Mining with Genetic Algorithms
 
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The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most `interesting' or `optimal' rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules.
Introduction  Distributed Data Mining
 
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Introduction Distributed Data Mining
Views: 382 Online Education
Secure Mining of Association Rules in Horizontally Distributed Databases
 
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We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton [18]. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. [8], which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol in [18]. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Views: 1666 Susmay FOURGREENSOFT
Secure Mining of Association Rules in Horizontally Distributed Databases
 
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We propose a protocol for secure mining of association rules in horizontally distributed databases. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Lecture 62 — The CURE Algorithm (Advanced) | Stanford University
 
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Secure Mining of Association Rules in Horizontally Distributed Databases
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Secure Mining of Association Rules in Horizontally Distributed Databases We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Views: 289 jpinfotechprojects
Hiding Sensitive Association Rules with Limited Side Effects.wmv
 
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Views: 359 projectsnine
Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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Secure Mining of Association Rules in Horizontally Distributed Databases
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Secure Mining of Association Rules in Horizontally Distributed Databases We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Views: 231 jpinfotechprojects
Final Year Projects | An Algorithm for Mining Association Rules Using Perfect Hashing and Database
 
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Final Year Projects | An Algorithm for Mining Association Rules Using Perfect Hashing and Database Prunin 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: 1328 Clickmyproject
Hardware enhanced association rule mining with Hashing and Pipelining 1
 
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Views: 432 PG Embedded Systems
Secure Mining of Association Rules in Horizontally Distributed Databases
 
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Views: 861 siva kumar
Secure Mining Of Association Rules In Horizontally Distributed Databases
 
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Views: 1415 Shiva Kumar
AN EFFICIENT MULTI-PARTY COMMUNICATION SCHEME WITH ASSOCIATION RULE MINING
 
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We propose a protocol for secure mining of association rules in horizontally distributed databases. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Secure Mining of Association Rules in Horizontally Distributed Databases (JAVA)
 
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Secure Mining of Association Rules inHorizontally Distributed Databases
 
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Views: 29 PROJECTS2014
Final Year Projects | Privacy-Preserving Mining of Association Rules From Outsourced Transaction
 
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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:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 846 myproject bazaar
Secure Mining of Association Rules in Horizontally Distributed Databases Java Project
 
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Project Link : http://kasanpro.com/p/java/secure-mining-association-rules-horizontally-distributed-databases , Title :Secure Mining of Association Rules in Horizontally Distributed Databases
Views: 119 kasanpro
Secure Mining of Association Rules in Horizontally Distributed  IN JAVA IEEE 2013 PROJECTS
 
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Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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Discovering association rules using the Apriori algorithm
 
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There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.
Data Mining with Weka (3.3: Using probabilities)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Using probabilities http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 27964 WekaMOOC
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
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Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Privacy Preserving Mining Of Association Rules From Outsourced Transaction Databases
 
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Views: 887 Shiva Kumar
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 258103 Last moment tuitions
Study of Database Intrusion Detection Based on Improved Association Rule Algorithm
 
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itle: Study of Database Intrusion Detection Based on Improved Association Rule Algorithm Domain: Data Mining Description: The proposed work is a hybrid approach that contains the detection of malicious and intrusive activity by combining two techniques, one is of association rule and second is Log mining. By combining these two methods we can achieve better efficiency by finding accurate intrusion in the database. The proposed method can be place on database management level and thus provide security to the database. The existing systems have limitations of missing few intrusions and high false positive rates and also they have overhead of creating profiles and keeping record of all the activities and update the large database every time. Intrusion detection technology refers to identify any activities of damage to the computer system security, integrity and confidentiality Different from the traditional operating system reinforcement, authentication and firewall security isolation technology, intrusion detection as an active dynamic security defence technologies, it provides internal attacks and external attacks and misuse in real-time protection. Data mining is an interdisciplinary field, affected by a number of disciplines, including database systems, statistics, machine learning, visualization and information science. There are many data mining methods commonly used in database intrusion detection, in which the association rule mining algorithm and sequential pattern mining algorithm are widely applied in particular. Association rule is to find the correlation of different items appeared in the same event. Association rule mining is to derive the implication relationships between data items under the conditions of a set of given project types and a number of records and through analyzing the records, the commonly used algorithm is Apriori algorithm. Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. contact for more details: 044-43548566,8110081181 [email protected]
Views: 114 SHPINE TECHNOLOGIES
Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
Efficient Algorithms For Mining High Utility Itemsets From Transactional Databases
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2013 Java: http://www.chennaisunday.com/ieee-2013-java-projects.html Out Put: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ IEEE 2013 Dot Net: http://www.chennaisunday.com/ieee-2013-Dotnet-projects.html IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html Out Put: http://www.youtube.com/channel/UC87_vSNJbLNmevUSseNE_vw IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html Out Put: http://www.youtube.com/channel/ UCLI3FPJiDQR6s6Y3BPsPqQ IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html Out Put: http://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 Model Video: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 1863 Shiva Kumar
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- 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 SOFTWARE ENGINEERING,COMPUTER GRAPHICS 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events 3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis 4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links 5. Amorphous Slicing of Extended Finite State Machines 6. Test Case-Aware Combinatorial Interaction Testing 7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions 8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling 9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler 10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis 11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis 12. Centroidal Voronoi Tessellations- A New Approach to Random Testing 13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm 14. Pair Programming and Software Defects--A Large, Industrial Case Study 15. Automated Behavioral Testing of Refactoring Engines 16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software 17. Self-Management of Adaptable Component-Based Applications 18. Elaborating Requirements Using Model Checking and Inductive Learning 19. Resource Management for Complex, Dynamic Environments 20. Identifying and Summarizing Systematic Code Changes via Rule Inference 21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications 22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers 23. On Fault Representativeness of Software Fault Injection 24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud 25. Coverage Estimation in Model Checking with Bitstate Hashing 26. Synthesizing Modal Transition Systems from Triggered Scenarios 27. Using Dependency Structures for Prioritization of Functional Test Suites INFORMATION AND COMMUNICATION SYSTEM 1. A Data Mining based Model for Detection of Fraudulent Behaviour in Water Consumption SERVICES COMPUTING 1. SVM-DT-Based Adaptive and Collaborative Intrusion Detection (jan 2018) 2. Cloud Workflow Scheduling With Deadlines And Time Slot Availability (March-April 1 2018) 3. Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing (17 May 2018) 4. Semantic-based Compound Keyword Search over Encrypted Cloud Data 5. Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based Approach 6. Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes
فیلم آموزشی جامع کاوش قواعد وابستگی یا Association Rule Mining (بخش دوم)
 
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برای کسب اطلاعات بیشتر، به این لینک مراجعه نمایید: http://www.matlabsite.com/mvrdm9206ij سرفصل های مورد بحث در این فیلم آموزشی عبارتند از: بررسی انواع الگوهای تکرار شونده ارائه مثال های پایه از تحلیل سبد خرید یا Market Basket Analysis معرفی قواعد توصیف کننده و خواص آن ها معرفی معیارهای تشخیص قاعده های قوی یا Strong Rules معرفی و بررسی کامل الگوریتم Apriori برای استخراج و کاوش قواعد وابستگی پیاده سازی گام به گام الگوریتم Apriori در محیط متلب به همراه حل یک مثال عملی بررسی ضعف های الگوریتم Apriori و مشکلات موجود در مسیر اجرای آن بررسی عوامل و معیارهای بهتر برای توصیف قواعد مهم و جالب توجه در پایگاه داده معرفی و بررسی الگوریتم رشد الگوی متداول یا Frequent Pattern Growth (به اختصار FP-Growth) بررسی مفاهیم تشکیل درخت FP-Tree و مزایای آن در مقایسه با Apriori پیاده سازی گام به گام الگوریتم FP-Growth در محیط متلب به همراه حل یک مثال عملی مدرس: سید مصطفی کلامی هریس کلمات کلیدی: Association Rule Mining, Data Mining, Data Mining in MATLAB, FP-Growth, Frequent Pattern, Frequent Pattern Growth, Frequent Rule, KDD, Knowledge Discovery, Knowledge Discovery from Data, Market Basket Analysis, Pattern Mining, Strong Rule, استخراج دانش, الگوریتم Apriori, الگوریتم رشد الگوی متداول, الگوهای تکرار شونده, تحلیل سبد خرید, تشکیل درخت FP-Tree, داده کاوی, داده کاوی در متلب, قواعد تکرار شونده, مبانی داده کاوی, معیارهای تشخیص قواعد قوی, معیارهای جذابیت قواعد, مقایسه Apriori و FP-Growth, پیاده سازی FP-Growth در متلب, پیاده سازی FP-Tree در متلب, پیاده سازی روش Apriori در متلب, کاوش الگو, کاوش دانش, کاوش قواعد تکرار شونده, کاوش قواعد وابستگی, کشف دانش
Views: 53 FaraDars
Efficient Techniques for Online Record Linkage
 
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Title: Efficient Techniques for Online Record Linkage Domain: Data Mining Key Features: 1. In recent years, heterogeneous data sources information merging is extensively required one. To achieve this goal, different types of heterogeneity problems should be resolved by an organization particularly the entity heterogeneity problem occurs when similar real-world entity type is signified using distinct identifier in various data sources. 2. To tackle this problem, statistical record linkage technique is used. But it fails when this statistical record linkage technique is used for online record linkage. It causes remarkable communication blockage in a disseminated environment (heterogeneity problems also arises). 3. To overcome this problem, a technique is proposed using the matching tree which resembles the same as decision tree .The proposed technique deduces the communication overhead drastically. The matching decision will be similar as result of conventional linkage technique. 4. The databases exhibiting entity heterogeneity are distributed, and it is not possible to create and maintain a central data repository or warehouse where precomputed linkage results can be stored. 5. An important issue associated with record linkage in distributed environments is that of schema integration. For record linkage techniques to work well, one should be able to identify the common nonkey attributes between two databases. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 2000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 2519 InnovationAdsOfIndia
Data Science Design Patterns
 
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Tennessee Leeuwenburg https://2016.pycon-au.org/schedule/78/view_talk Most 'data science' projects fall into just a few well-understood design patterns. This talk de-mystifies what those patterns are, how to use them practically, and how to get to grips with your data. We'll a look at how to understand the input/output structure of the models, how to design a reasonable 'experiment', and how to get started. We'll look at getting to grips with problems by simple data sets that can fit entirely on-screen, designing the basic 'form' of the machine before levelling up to bigger data and badder algorithms. All of this will be shown using Python tools, libraries and running code.
Views: 1462 PyCon Australia
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 326781 Last moment tuitions
Big Data Technologies
 
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Big Data Technologies Watch more Videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited
Final Year Projects | Efficient Mining of Freqent Itemsets on large uncertain databases
 
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Views: 246 Clickmyproject
Java basic tutorial 09: Association One To One
 
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BITM Mobile Apps Development Class 05 Topic: Association One To One
Views: 116 Mahmudul Hasan
FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce
 
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FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce
Simple Explanation of Chi-Squared
 
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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 1002458 J David Eisenberg
A frequent itemsets mining algorithm based on matrix in sliding window over data streams
 
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Views: 338 ranjith kumar
Neural Network Explained -Artificial Intelligence - Hindi
 
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Neural network in ai (Artificial intelligence) Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain. Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons. Neuron are in massive therefore they provide distributed network. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 10565 CaelusBot