Approximately mining recently representative patterns on data streams

Jia-Ling Koh*, Yuan Bin Don

*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

3 引文 斯高帕斯(Scopus)

摘要

Catching the recent trend of data is an important issue when mining frequent itemsets from data streams. To prevent from storing the whole transaction data within the sliding window, the frequency changing point (FCP) method was proposed for monitoring the recent occurrences of itemsets in a data stream under the assumption that exact one transaction arrives at each time point. In this paper, the FCP method is extended for maintaining recent patterns in a data stream where a block of various numbers of transactions (including zero or more transactions) is inputted within each time unit. Moreover, to avoid generating redundant information in the mining results, the recently representative patterns are discovered from the maintained structure approximately. The experimental results show that our approach reduces the run-time memory usage significantly. Moreover, the proposed GFCP algorithm achieves high accuracy of mining results and guarantees no false dismissal occurring.

原文英語
主出版物標題Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers
頁面231-243
頁數13
出版狀態已發佈 - 2007 十二月 1
事件Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, 中国
持續時間: 2007 五月 222007 五月 22

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4819 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

其他

其他Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
國家/地區中国
城市Nanjing
期間2007/05/222007/05/22

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

指紋

深入研究「Approximately mining recently representative patterns on data streams」主題。共同形成了獨特的指紋。

引用此