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Cluster analysis and data mining : an introduction /
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 cl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Dulles, Virginia ; Boston, Massachusetts ; New Delhi :
Mercury Learning and Information,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a King, Ronald S., |d 1943- |e author. | |
| 245 | 1 | 0 | |a Cluster analysis and data mining : |b an introduction / |c R.S. King. |
| 264 | 1 | |a Dulles, Virginia ; |a Boston, Massachusetts ; |a New Delhi : |b Mercury Learning and Information, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource : |b illustrations | ||
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| 588 | 0 | |a Online resource; title from title screen (Books24x7.com, viewed January 31, 2015). | |
| 520 | |a 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. -- |c Edited summary from book. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Introduction to cluster analysis -- Overview of data mining -- Hierarchical clustering -- Partition clustering -- Judgmental analysis -- Fuzzy clustering models and applications -- Classification and association rules -- Cluster validity -- Clustering categorical data -- Mining outliers -- Model-based clustering -- General issues. | |
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| 650 | 0 | |a Cluster analysis. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Classification automatique (Statistique) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Cluster analysis |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
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