Exception rule mining with a relative interestingness measure

Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

77 被引用数 (Scopus)


This paper presents a method for mining exception rules based on a novel measure which estimates interestingness relative to its corresponding common sense rule and reference rule. Mining interesting rules is one of the important data mining tasks. Interesting rules bring novel knowledge that helps decision makers for advantageous actions. It is true that interestingness is a relative issue that depends on the other prior knowledge. However, this estimation can be biased due to the incomplete or inaccurate knowledge about the domain. Even if possible to estimate interestingness, it is not so trivial to judge the interestingness from a huge set of mined rules. Therefore, an automated system is required that can exploit the knowledge extractacted from the data in measuring interestingness. Since the extraicted knowledge comes from the data, so it is possible to find a measure that is unbiased from the user's own belief. An unbiased measure that can estimate the interestingness of a rule with respect to the extractacted rules can be more acceptable to the user. In this work we try to show through the experiments, how our proposed relative measure can give an unbiased estimate of relative interestingness in a rule considering already mined rules.

ホスト出版物のタイトルKnowledge Discovery and Data Mining
ホスト出版物のサブタイトルCurrent Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
編集者Takao Terano, Huan Liu, Arbee L.P. Chen
出版社Springer Verlag
ISBN(印刷版)3540673822, 9783540673828
出版ステータス出版済み - 2000
イベント4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, 日本
継続期間: 4月 18 20004月 20 2000


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


その他4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


「Exception rule mining with a relative interestingness measure」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。