"...a very readable and up-to-date introduction to data mining..." (Quality & Reliability Engineering International, Vol. 21 (4) June 2005) "...suitable for a graduate level course in data mining...I enjoyed reading this book and recommend it highly." (Journal of Statistical Computation & Simulation, April 2004) "...clear and well understandable...recommended as basic guidance...practitioners will profit from the author's long experience..." (Zentralblatt Math, Vol. 1027, 2004) "...reviews state-of-the-art techniques for analyzing enormous quantities of raw data..." (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003) "...a valuable book... I truly enjoyed reading the book and I am glad to recommend it to anyone working in this fascinating field." (IIE Transactions) "...detailed, well illustrated, and easy to understand...comprehensive...a good book..." (Mathematical Reviews 2003h) "...this is probably the first data-mining book that I would select from my bookshelf as reading material for a statistician..." (Technometrics, Vol. 45, No. 3, August 2003)
This book offers a comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. "Data Mining: Concepts, Models, Methods, and Algorithms" discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine.
This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.