Sequential pattern mining with time intervals

Yu Hirate, Hayato Yamana

研究成果: Conference contribution

17 被引用数 (Scopus)

抄録

Sequential pattern mining can be used to extract frequent sequences maintaining their transaction order. As conventional sequential pattern mining methods do not consider transaction occurrence time intervals, it is impossible to predict the time intervals of any two transactions extracted as frequent sequences. Thus, from extracted sequential patterns, although users are able to predict what events will occur, they are not able to predict when the events will occur. Here, we propose a new sequential pattern mining method that considers time intervals. Using Japanese earthquake data, we confirmed that our method is able to extract new types of frequent sequences that are not extracted by conventional sequential pattern mining methods.

本文言語English
ホスト出版物のタイトルAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
ページ775-779
ページ数5
DOI
出版ステータスPublished - 2006 7 14
イベント10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
継続期間: 2006 4 92006 4 12

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3918 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
CountrySingapore
CitySingapore
Period06/4/906/4/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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