Classification
著者
書誌事項
Classification
(Monographs on statistics and applied probability, 82)
Chapman & Hall/CRC, c1999
2nd ed
大学図書館所蔵 全30件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Bibliography: p. [213]-241
Includes indexes
内容説明・目次
内容説明
As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects.Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site.Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.
目次
Introduction Classification, Assignment, and Dissection Aims of Classification Stages in a Numerical Classification Data Sets Measures of Similarity and Dissimilarity Introduction Selected Measures of Similarity and Dissimilarity Some Difficulties Construction of Relevant Measures Partitions Partitioning Criteria Iterative Relocation Algorithms Mathematical Programming Other Partitioning Algorithms How Many Clusters? Links with Statistical Models Hierarchical Classifications Definitions and Representations Algorithms Choice of Clustering Strategy Consensus Trees More General Tree Models Other Clustering Procedures Fuzzy Clustering Constrained Classification Overlapping Classification Conceptual Clustering Classification of Symbolic Data Partitions of Partitions Graphical Representations Introduction Principal Coordinates Analysis Non-Metric Multidimensional Scaling Interactive Graphics and Self-Organizing Maps Biplots Cluster Validation and Description Introduction Cluster Validation Cluster Description References Author Index Subject Index
「Nielsen BookData」 より