Data Mining Techniques By Arun K Pujari Pdf

[Murthy, 1998] Murthy S. K.: Automatic Construction of decision trees from Data: A Multidisciplinary Survey, Data Mining and Knowledge Discovery 2, 1998. [Needleman, 1970] Needleman S. And Wunsch, C. D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins.

Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms. The book contains the algorithmic details of different techniques such as A priori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE, SPIRIT, etc. Interesting and recent developments such as Support Vector Machines and Rough Set Theory are also covered in the book.

The book also discusses the mining of web data, spatial data, temporal data and text data. This book can serve as a textbook for students of computer science, mathematical science and management science.

It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory. The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and artificial intelligence technique. The discussion on association rule mining has been extended to include rapid association rule mining (RARM), FP-Tree Growth Algorithm for discovering association rule and the Eclat and dEclat algorithms. These appear in Chapter 4.

'synopsis' may belong to another edition of this title. About the Author: Arun K Pujari is Professor of Computer Science at the University of Hyderabad, Hyderabad. Prior to joining the university, he served at the Automated Cartography Cell, Survey of India, Dehradun, and Jawaharlal Nehru University, New Delhi. He received his PhD from the Indian Institute of Technology Kanpur and MSc from Sambalpur University, Sambalpur. He has also been on visiting ssignments to the Institute of Industrial Sciences, University of Tokyo; International Institute of Software Technology, United Nations University, Macau; University of Memphis, USA; and Griffith University, Australia, among others.

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Professor Pujari is at present the vice-chancellor of Sambalpur University. 'About this title' may belong to another edition of this title. Book Description Orient BlackSwan/ Universities Press, 2010.

Condition: New. Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms. The book contains the algorithmic details of different techniques such as A priori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE, SPIRIT, etc.

Interesting and recent developments such as Support Vector Machines and Rough Set Theory are also covered in the book. The book also discusses the mining of web data, spatial data, temporal data and text data. This book can serve as a textbook for students of computer science, mathematical science and management science. It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory. The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and artificial intelligence technique. The discussion on association rule mining has been extended to include rapid association rule mining (RARM), FP-Tree Growth Algorithm for discovering association rule and the Eclat and dEclat algorithms.

These appear in Chapter 4. Printed Pages: 340. Seller Inventory # 18964 .

Book Description Orient BlackSwan/ Universities Press, 2010. Condition: New. Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms.

The book contains the algorithmic details of different techniques such as A priori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE, SPIRIT, etc. Interesting and recent developments such as Support Vector Machines and Rough Set Theory are also covered in the book. The book also discusses the mining of web data, spatial data, temporal data and text data. This book can serve as a textbook for students of computer science, mathematical science and management science. It can also be an excellent handbook for researchers in the area of data mining and data warehousing.