Discovering maximum sequential patterns efficiently | |
Lu, S ; Li, CP | |
2010-05-07 ; 2010-05-07 | |
会议名称 | DMIN '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON DATA MINING ; International Conference on Data Mining (DMIN 05) ; Las Vegas, NV ; Web of Science |
关键词 | sequential patterns statistical model data mining Computer Science, Artificial Intelligence Computer Science, Information Systems |
中文摘要 | By analyzing the Apriori-like techniques [2, 3, 4] and the features of discovering frequent patterns on the large sets of transactions, we present an improved algorithm - AprioriAdjust based on a statistical model to decide the sequenced support, in which the convergence of average value of the support in the whole procedure is considered. Our aim is to,develop a feasible approach for pruning the candidate set of the frequent patterns efficiently and furthermore to improve the performance of discovering maximum sequential patterns. |
会议录出版者 | C S R E A PRESS ; ATHENS ; 115 AVALON DR, ATHENS, GA 30606 USA |
语种 | 英语 ; 英语 |
内容类型 | 会议论文 |
源URL | [http://hdl.handle.net/123456789/16942] ![]() |
专题 | 清华大学 |
推荐引用方式 GB/T 7714 | Lu, S,Li, CP. Discovering maximum sequential patterns efficiently[C]. 见:DMIN '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON DATA MINING, International Conference on Data Mining (DMIN 05), Las Vegas, NV, Web of Science. |
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