Clustering for Data Mining: A Data Recovery Approach

Boris Mirkin

ISBN 10: 1584885343 / ISBN 13: 9781584885344

Editore: CHAPMAN & HALL, 2005

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Zustand: Gut | Sprache: Englisch | Produktart: Bücher.

Codice articolo 2190511/203

Dati bibliografici

• Titolo: Clustering for Data Mining: A Data Recovery Approach
• Editore: CHAPMAN & HALL
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 1584885343
• ISBN 13: 9781584885344
• Rilegatura: Rilegato
• Condizione: Gut
• Cataloghi dei venditori:
  • Bücher

Descrizione articolo

Riassunto

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 reference.

Contenuti

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 Words