An approximate approach for mining recently frequent itemsets from data streams

Jia Ling Koh*, Shu Ning Shin

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

11 引文 斯高帕斯(Scopus)

摘要

Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that the embedded knowledge in a data stream will change quickly as time goes by. Therefore, catching the recent trend of data is an important issue when mining frequent itemsets from data streams. Although the sliding window model proposed a good solution for this problem, the appearing information of the patterns within the sliding window has to be maintained completely in the traditional approach. In this paper, for estimating the approximate supports of patterns within the current sliding window, two data structures are proposed to maintain the average time stamps and frequency changing points of patterns, respectively. The experiment results show that our approach will reduce the run-time memory usage significantly. Moreover, the proposed FCP algorithm achieves high accuracy of mining results and guarantees no false dismissal occurring.

原文英語
主出版物標題Data Warehousing and Knowledge Discovery - 8th International Conference, DaWaK 2006, Proceedings
發行者Springer Verlag
頁面352-362
頁數11
ISBN(列印)3540377360, 9783540377368
DOIs
出版狀態已發佈 - 2006
事件8th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2006 - Krakow, 波兰
持續時間: 2006 九月 42006 九月 8

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4081 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

其他

其他8th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2006
國家/地區波兰
城市Krakow
期間2006/09/042006/09/08

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

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