Clustering for Data Mining

Clustering for Data Mining : A Data Recovery Approach

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Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods - K-Means for partitioning the data set and Ward's method for hierarchical clustering - have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids. Rather than the traditional set of ad hoc techniques, "Clustering for Data Mining: A Data Recovery Approach" presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering.The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre and post-processing issues that are beyond the scope of most texts, and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional more

Product details

  • Hardback | 296 pages
  • 162 x 234 x 24mm | 557.93g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • United States
  • English
  • 36 black & white illustrations, 81 black & white tables, 1 black & white halftones
  • 1584885343
  • 9781584885344

Table of contents

INTRODUCTION: HISTORICAL REMARKS WHAT IS CLUSTERING Exemplary Problems Bird's Eye View WHAT IS DATA Feature Characteristics Bivariate Analysis Feature Space and Data Scatter Preprocessing and Standardizing Mixed Data K-MEANS CLUSTERING Conventional K-Means Initialization of K-Means Intelligent K-Means Interpretation Aids Overall Assessment WARD HIERARCHICAL CLUSTERING Agglomeration: Ward Algorithm Divisive Clustering with Ward Criterion Conceptual Clustering Extensions of Ward Clustering Overall Assessment DATA RECOVERY MODELS Statistics Modeling as Data Recovery Data Recovery Model for K-Means Data Recovery Models for Ward Criterion Extensions to Other Data Types One-by-One Clustering Overall Assessment DIFFERENT CLUSTERING APPROACHES Extensions of K-Means Clustering Graph-Theoretic Approaches Conceptual Description of Clusters Overall Assessment GENERAL ISSUES Feature Selection and Extraction Data Pre-Processing and Standardization Similarity on Subsets and Partitions Dealing with Missing Data Validity and Reliability Overall Assessment CONCLUSION: Data Recovery Approach in Clustering BIBLIOGRAPHY Each chapter also contains a section of Base Wordsshow more

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