Grouping multidimensional data : recent advances in clustering
著者
書誌事項
Grouping multidimensional data : recent advances in clustering
Springer, c2006
大学図書館所蔵 件 / 全8件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. [239]-264) and index
内容説明・目次
内容説明
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.
目次
The Star Clustering Algorithm for Information Organization.- A Survey of Clustering Data Mining Techniques.- Similarity-Based Text Clustering: A Comparative Study.- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning.- Clustering with Entropy-Like k-Means Algorithms.- Sampling Methods for Building Initial Partitions.- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections.- Criterion Functions for Clustering on High-Dimensional Data.
「Nielsen BookData」 より
