Go Green: Recycle and Reuse Frequent Patterns

In constrained data mining, users can specify constraints that can be used to prune the search space to avoid mining uninteresting knowledge. Since it is difficult to determine the precise values of the constraints to be used, users typically refine these values iteratively until satisfactory results are obtained. Existing mining schemes treat each iteration as a distinct mining process, and fail to exploit the information generated between iterations. In this paper, we propose to salvage knowledge that is discovered from an earlier iteration of mining to enhance subsequent rounds of mining. In particular, we look at how frequent patterns can be recycled. Our proposed strategy operates in two phases. In the first phase, frequent patterns obtained from an early iteration are used to compress a database. In the second phase, subsequent mining processes operate on the compressed database. We propose two compression strategies and adapt three existing frequent pattern mining techniques to...

• Type: Conference
• Year: 2004
• Where: ICDE
• Authors: Gao Cong, Beng Chin Ooi, Kian-Lee Tan, Anthony K. H. Tung
• Keywords: Database, Frequent Pattern Mining, ICDE 2004, Mining Uninteresting Knowledge, Subsequent Mining Processes