Improved sequential pattern mining using an extended bitmap representation

Chien Liang Wu*, Jia Ling Koh, Pao Ying An


研究成果: 雜誌貢獻會議論文同行評審

5 引文 斯高帕斯(Scopus)


The main challenge of mining sequential patterns is the high processing cost of support counting for large amount of candidate patterns. For solving this problem, SPAM algorithm was proposed in SIGKDD'2002, which utilized a depth-first traversal on the search space combined with a vertical bitmap representation to provide efficient support counting. According to its experimental results, SPAM outperformed the previous works SPADE and PrefixSpan algorithms on large datasets. However, the SPAM algorithm is efficient under the assumption that a huge amount of main memory is available such that its practicability is in question. In this paper, an Improved-version of SPAM algorithm, called I-SPAM, is proposed. By extending the structures of data representation, several heuristic mechanisms are proposed to speed up the efficiency of support counting further. Moreover, the required memory size for storing temporal data during mining process of our method is less than the one needed by SPAM. The experimental results show that I-SPAM can achieve the same magnitude efficiency and even better than SPAM on execution time under about half the maximum memory requirement of SPAM.

頁(從 - 到)776-785
期刊Lecture Notes in Computer Science
出版狀態已發佈 - 2005
事件16th International Conference on Database and Expert Systems Applications, DExa 2005 - Copenhagen, 丹麦
持續時間: 2005 8月 222005 8月 26

ASJC Scopus subject areas

  • 理論電腦科學
  • 一般電腦科學


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