標題: Flexible online association rule mining based on multidimensional pattern relations
作者: Wang, CY
Tseng, SS
Hong, TP
資訊工程學系
Department of Computer Science
關鍵字: data mining;association rule;incremental mining;multidimensional mining;constraint-based mining;data warehouse
公開日期: 22-六月-2006
摘要: Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach. (c) 2005 Elsevier Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ins.2005.05.005
http://hdl.handle.net/11536/12136
ISSN: 0020-0255
DOI: 10.1016/j.ins.2005.05.005
期刊: INFORMATION SCIENCES
Volume: 176
Issue: 12
起始頁: 1752
結束頁: 1780
顯示於類別:期刊論文


文件中的檔案:

  1. 000237706400007.pdf