Discovering Knowledge in Data: An Introduction to Data Mining

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John Wiley & Sons, 28 de jan. de 2005 - 222 páginas
Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
 

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Conteúdo

1 INTRODUCTION TO DATA MINING
1
2 DATA PREPROCESSING
27
3 EXPLORATORY DATA ANALYSIS
41
4 STATISTICAL APPROACHES TO ESTIMATION AND PREDICTION
67
5 kNEAREST NEIGHBOR ALGORITHM
90
6 DECISION TREES
107
7 NEURAL NETWORKS
128
8 HIERARCHICAL AND kMEANS CLUSTERING
147
9 KOHONEN NETWORKS
163
10 ASSOCIATION RULES
180
11 MODEL EVALUATION TECHNIQUES
200
EPILOGUE WEVE ONLY JUST BEGUN
215
INDEX
217
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Sobre o autor (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.

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