Temporal data mining
著者
書誌事項
Temporal data mining
(Chapman & Hall/CRC data mining and knowledge discovery series)
Chapman & Hall/CRC, c2010
- : hbk
並立書誌 全1件
-
-
Temporal data mining / Theophano Mitsa
BB09492823
-
Temporal data mining / Theophano Mitsa
大学図書館所蔵 全8件
  青森
  岩手
  宮城
  秋田
  山形
  福島
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  埼玉
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  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
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  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.
From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining.
Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/
目次
Temporal Databases and Mediators. Temporal Data Similarity Computation, Representation, and Summarization. Temporal Data Classification and Clustering. Prediction. Temporal Pattern Discovery. Temporal Data Mining in Medicine and Bioinformatics. Temporal Data Mining and Forecasting in Business and Industrial Applications. Web Usage Mining. Spatiotemporal Data Mining. Appendices. Index.
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