Discovering Knowledge in Data: An Introduction to Data Mining.pdf
"...an excellent introductory book of data mining. I recommend it for every one who wants to learn data mining." (Journal of Statistical Software, May 2006) "...selected material is described in a simple, clear, and!precise way...case studies!examples, and screen shots has definitely added to the learning value of the book." (Journal of Biopharmaceutical Statistics, January/February 2006) "...does a good job introducing data mining to novices...it skillfully previews some of the basic statistical issues needed to understand data mining techniques." (Journal of the American Statistical Association, December 2005) "If you need a book to help colleagues understand your data mining procedures and results, this is the one you want to give them." (Technometrics, November 2005) "!an excellent book!it should be useful for anyone interested in analysing epidemiological data." (Statistics in Medical Research, October 2005) "...an excellent a white--boxa overview of established approaches for data analysis, in which readers are shown how, why, and when the methods work." (CHOICE, April 2005) "Larose has the making of a good series of books on data mining!I, for one, look forward to the next two books in the series." (Computing Reviews.com, February 15, 2005)
DANIEL T. LAROSE received his PhD in statistics from the University of Connecticut. An associate professor of statistics at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world's first online master of science program in data mining. He has also worked as a data mining consultant for Connecticut-area companies. He is currently working on the next two books of his three-volume series on Data Mining: Data Mining Methods and Models and Data Mining the Web: Uncovering Patterns in Web Content, scheduled to publish respectively in 2005 and 2006.
1. An Introduction to Data Mining.
2. Data Preprocessing.
3. Exploratory Data Analysis.
4. Statistical Approaches to Estimation and Prediction.
5. k-Nearest Neighbor.
6. Decision Trees.
7. Neural Networks.
8. Hierarchical and k-Means Clustering.
9. Kohonen networks.
10. Association Rules.
11. Model Evaluation Techniques.
Epilogue: "We've Only Just Begun".
This is a new edition of a highly praised, successful reference on data mining, now more important than ever due to the growth of the field and wide range of applications. This edition features new chapters on multivariate statistical analysis, covering analysis of variance and chi-square procedures; cost-benefit analyses; and time-series data analysis. There is also extensive coverage of the R statistical programming language. Graduate and advanced undergraduate students of computer science and statistics, managers/CEOs/CFOs, marketing executives, market researchers and analysts, sales analysts, and medical professionals will want this comprehensive reference.