Data Mining Techniques

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 Apriori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE and SPIRIT. Interesting and recent developments such as support vector machines and rough set theory are also covered. The book also discusses the mining of web data, spatial data, temporal data and text data. The inclusion of well thought out illustrated examples for making the concepts clear to a first time reader makes the book suitable as a textbook for students of computer science, mathematical science and management science. It can also serve as a handbook for researchers in the area of data mining and data warehousing. In this edition, the chapter on data warehousing has been thoroughly revised and its scope of coverage expanded to include a detailed discussion on multidimensional data modelling and cube computation. The discussion on genetic algorithms too has been considerably expanded to bring to fore its applications in the context of data mining.