Publisher's Synopsis
This second edition of Data Mining Methods discusses both theoretical foundation and practical applications of data mining in a web field including banking, e-commerce, medicine, engineering and management. This book starts by introducing data and information, basic data type, data category and applications of data mining. The second chapter briefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statistics are discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an in-depth and useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabular method for decision tree building is discussed. The chapter on association rules discusses popular algorithms and compares various algorithms in summary table form. An interesting application of genetic algorithm is introduced in the next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived in the appendix. Popular clustering algorithm is discussed in the next chapter. The web mining chapter generalizes the page rank metric in multiple ways. A geometric derivation of SDM appear next and summary table in table form is given. LSI indexing for IRN extension is discussed next. The book ends with a thorough discussion of text mining metrics and gives latest research directions in text mining.
