Recent Advances in Clustering

Grouping Multidimentional Data: Recent Advances in Clustering
Jacob Kogan, Charles Nicholas, Marc Teboulle | Springer | ISBN-13 978-3-540-28348-5 | 272 pgs | 8 mb

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It can be used as an independent data mining task to disclose intrinsic characteristics of data, or as a preprocessing step with the clustering results used further in other data mining tasks, such as classification, prediction, correlation analysis, and anomaly detection. It is no wonder that clustering has been studied extensively in various research fields, including data mining, machine learning, pattern recognition, and scientific, engineering, social, economic, and biomedical data analysis. Although there have been numerous studies on clustering methods and their applications, due to the wide spectrum that the theme covers and the diversity of the methodology research publications on this theme have been scattered in various conference proceedings or journals in multiple research fields. There is a need for a good collection of books dedicated to this theme, especially considering the surge of research activities on cluster analysis in the last several years.

This book fills such a gap and meets the demand of many researchers and practitioners who would like to have a solid grasp of the state of the art on cluster analysis methods and their applications. The book consists of a collection of chapters, contributed by a group of authoritative researchers in the field. It covers a broad spectrum of the field, from comprehensive surveys to in-depth treatments of a few important topics. The book is organized in a systematic manner, treating different themes in a balanced way. It is worth reading and further when taken as a good reference book on your shelf.
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