獲得した情報を用いる遺伝的ネットワークプログラミングによるデータマイニング  [in Japanese] Data Mining Using Genetic Network Programming with the Use of Acquired Information  [in Japanese]

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Author(s)

    • 嶋田 香 SHIMADA KAORU
    • 早稲田大学大学院情報生産システム研究科 Graduate School of Information, Production and Systems, Waseda University
    • 古月 敬之 FURUZUKI TAKAYUKI
    • 早稲田大学大学院情報生産システム研究科 Graduate School of Information, Production and Systems, Waseda University

Abstract

遺伝的ネットワークプログラミング(Genetic Network Programming,GNP)を用いた興味深い相関ルールの抽出法を提案する.統計学で用いられるχ2値を指標の一部とした興味深い相関ルールを進化論的計算手法によって抽出する.相関ルールの指標はGNPの構造的な特徴を利用して算出される.ルール抽出は世代継続的に行われるため抽出された相関ルールはライブラリに蓄積される.抽出された相関ルールに関する情報は,抽出を継続中のGNPの個体評価および進化操作時に用いられる.したがって,本手法は通常の進化論的計算手法とは進化の方法が異なる.シミュレーション結果から,本手法が興味深い相関ルールの抽出を効率的に行うことが示された.A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of rule extraction. The proposed system evolves itself by an evolutionary method and measures the significance of the association via the chi-squared test using GNP. Extracted association rules are stored in a pool all together through generations in order to find new important rules. These rules are reflected in genetic operators as acquired information. Therefore, the proposed method is fundamentally different from all other evolutionary methods in its evolutionary way. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of rule extraction. The proposed system evolves itself by an evolutionary method and measures the significance of the association via the chi-squared test using GNP. Extracted association rules are stored in a pool all together through generations in order to find new important rules. These rules are reflected in genetic operators as acquired information. Therefore, the proposed method is fundamentally different from all other evolutionary methods in its evolutionary way. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

Journal

  • IPSJ journal

    IPSJ journal 46(10), 2576-2586, 2005-10-15

    Information Processing Society of Japan (IPSJ)

Cited by:  4

Codes

  • NII Article ID (NAID)
    110002769905
  • NII NACSIS-CAT ID (NCID)
    AN00116647
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    1882-7764
  • NDL Article ID
    7490784
  • NDL Source Classification
    ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL Call No.
    Z14-741
  • Data Source
    CJPref  NDL  NII-ELS  IPSJ 
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