| Description |
1 online resource : illustrations |
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1 online resource |
| Physical Medium |
polychrome |
| Description |
text file |
| Summary |
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 computer sciences. This book is applicable to either a course on clustering and classification or as a companion text for a first class in applied statistics. This book puts emphasis on illustrating the underlying logic in making decisions during the cluster analysis; brings out the related applications of statistics: Ward's method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.); and includes separate chapters on JAN and the clustering of categorical data. -- Edited summary from book. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Title; Copyright; Dedication; Contents; Preface; Chapter 1: Introduction to Cluster Analysis; 1.1 What Is a Cluster?; 1.2 Capturing the Clusters; 1.3 Need for Visualizing Data; 1.4 The Proximity Matrix; 1.5 Dendrograms; 1.6 Summary; 1.7 Exercises; Chapter 2: Overview of Data Mining; 2.1 What Is Data Mining?; 2.2 Data Mining Relationship to Knowledge Discovery in Databases; 2.3 The Data Mining Process; 2.4 Databases and Data Warehousing; 2.5 Exploratory Data Analysis and Visualization; 2.6 Data Mining Algorithms; 2.7 Modeling for Data Mining; 2.8 Summary; 2.9 Exercises |
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Chapter 3: Hierarchical Clustering3.1 Introduction; 3.2 Single-Link versus Complete-Link Clustering; 3.3 Agglomerative versus Divisive Clustering; 3.4 Ward's Method; 3.5 Graphical Algorithms for Single-Link versus Complete-Link Clustering; 3.6 Summary; 3.7 Exercises; Chapter 4: Partition Clustering; 4.1 Introduction; 4.2 Iterative Partition Clustering Method; 4.3 The Initial Partition; 4.4 The Search for Poor Fits; 4.5 K-Means Algorithm; 4.5.1 MacQueen's Method; 4.5.2 Forgy's Method; 4.5.3 Jancey's Method; 4.6 Grouping Criteria; 4.7 BIRCH, a Hybrid Method; 4.8 Summary; 4.9 Exercises |
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Chapter 5: Judgmental Analysis5.1 Introduction; 5.2 Judgmental Analysis Algorithm; 5.2.1 Capturing R2; 5.2.2 Grouping to Optimize Judges' R2; 5.2.3 Alternative Method for JAN; 5.3 Judgmental Analysis in Research; 5.4 Example JAN Study; 5.4.1 Statement of Problem; 5.4.2 Predictor Variables; 5.4.3 Criterion Variables; 5.4.4 Questions Asked; 5.4.5 Method Used for Organizing Data; 5.4.6 Subjects Judged; 5.4.7 Judges; 5.4.8 Strategy Used for Obtaining Data; 5.4.9 Checking the Model; 5.4.10 Extract the Equation; 5.5 Summary; 5.6 Exercises; Chapter 6: Fuzzy Clustering Models and Applications |
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6.1 Introduction6.2 The Membership Function; 6.3 Initial Configuration; 6.4 Merging of Clusters; 6.5 Fundamentals of Fuzzy Clustering; 6.6 Fuzzy C-Means Clustering; 6.7 Induced Fuzziness; 6.8 Summary; 6.9 Exercises; Chapter 7: Classification and Association Rules; 7.1 Introduction; 7.2 Defining Classification; 7.3 Decision Trees; 7.4 ID3 Tree Construction Algorithm; 7.4.1 Choosing the "Best" Feature; 7.4.2 Information Gain Algorithm; 7.4.3 Tree Pruning; 7.5 Bayesian Classification; 7.6 Association Rules; 7.7 Pruning; 7.8 Extraction of Association Rules; 7.9 Summary; 7.10 Exercises |
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Chapter 8: Cluster Validity8.1 Introduction; 8.2 Statistical Tests; 8.3 Monte Carlo Analysis; 8.4 Indices of Cluster Validity; 8.5 Summary; 8.6 Exercises; Chapter 9: Clustering Categorical Data; 9.1 Introduction; 9.2 ROCK; 9.3 STIRR; 9.4 CACTUS; 9.5 CLICK; 9.6 Summary; 9.7 Exercises; Chapter 10: Mining Outliers; 10.1 Introduction; 10.2 Outlier Detection Methods; 10.3 Statistical Approaches; 10.4 Outlier Detection by Clustering; 10.5 Fuzzy Clustering Outlier Detection; 10.6 Summary; 10.7 Exercises; Chapter 11: Model-based Clustering; 11.1 Introduction |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Cluster analysis.
|
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Cluster analysis. |
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Data mining.
|
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Data mining. |
| Genre/Form |
Electronic books.
|
| ISBN |
9781938549397 (electronic book) |
|
1938549392 (electronic book) |
|
9781942270133 |
|
1942270135 |
|
9781938549380 |
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