Incrementally mining recently repeating patterns over data streams

Jia Ling Koh, Pei Min Chou

研究成果: 書貢獻/報告類型會議論文篇章


Repeating patterns represent temporal relations among data items, which could be used for data summarization and data prediction. More and more data of various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns over the stream. Therefore, the traditional strategies for mining repeating patterns on static database are not applicable to data streams. For this reason, an algorithm, named appearing-bit-sequence-based incremental mining algorithm, for efficiently discovering recently repeating patterns over a data stream is proposed in this paper. The appearing bit sequences are used to monitor the occurrences of patterns within a sliding window. Two versions of algorithms are proposed by maintaining the appearing bit sequences of maximum repeating patterns and closed repeating patterns, respectively. Accordingly, the cost of re-mining repeating patterns over a sliding window is reduced to that of monitoring frequency changes of the maintained patterns. The experimental results show that the incremental mining methods perform much better than the re-miming approach.

主出版物標題New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers
發行者Springer Verlag
ISBN(列印)3642003982, 9783642003981
出版狀態已發佈 - 2009
事件Pacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, 日本
持續時間: 2008 5月 202009 5月 23


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5433 LNAI


其他Pacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)


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