Grouping Multidimensional Data
Subtitle: Recent Advances in Clustering
Contributors: Jacob Kogan (Editor); Charles Nicholas (Editor); Marc Teboulle (Editor)
Edition: 1 edition
Publisher: Springer
Published: February 10, 2006
Language: eng
Format: Hardcover
Number of pages: 268
ISBN-13: 9783540283485
Subjects: Databases & data structures; Computers - General Information; Computer Science; Computers; Computer Books: General; Information Storage & Retrieval; Computer Vision; Computers / Information Storage & Retrieval; data analysis; data clustering; text clustering; text mining; Classification automatique (Statistique); Cluster analysis; Data mining
Description: Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.