Cluster Analysis and Data Mining: An Introduction

Cluster Analysis and Data Mining: An Introduction

by Ronald S. King

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Overview

Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc.

eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.

FEATURES

*Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis

*Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.)

*Contains separate chapters on JAN and the clustering of categorical data

*Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.




Product Details

ISBN-13: 9781938549380
Publisher: Mercury Learning & Information
Publication date: 11/30/2014
Edition description: New Edition
Pages: 300
Product dimensions: 7.00(w) x 8.90(h) x 0.80(d)

About the Author

Ronald S. King holds a PhD in applied statistics and currently teaches online courses for Tarleton State University (TX). Spanning a career of four decades of teaching and administration at multiple universities, he brings a unique perspective to the fields of statistics, computer science, and information systems. His lifetime career publications have made numerous contributions to these fields.

Table of Contents

1) Introduction to Cluster Analysis
2) Overview of Data Mining
3) Hierarchical Clustering
4) Partition Clustering
5) Judgmental Analysis
6) Fuzzy Clustering Models and Applications
7) Classification and Association Rules
8) Cluster Validity
9) Clustering Categorical Data
10) Mining Outliers
11) Model-Based Clustering
12) General Issues

Appendices
Index

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