Go Green : Recycle and Reuse Frequent Patterns

• Item type: SIG Technical Reports(1)
• 公開日: 2004-07-14
• タイトル: Go Green : Recycle and Reuse Frequent Patterns
• タイトル / 言語: en
• タイトル: Go Green : Recycle and Reuse Frequent Patterns
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: National University of Singapore
• 著者所属(英): en
• 著者所属(英): National University of Singapore
• 著者名: TungKumHoe
• 著者名(英): Tung, KumHoe
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In constrained data mining user can specify constraints to prune the search space to avoid mining uninteresting knowledge. This is typically done by specifying some initial values of the constraints that are subsequently refinditeratively 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 talk we will look at how we can 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 exploit the compressed database. Results from our extensive experimental study show that our proposed recycling algorithm outperform their non-recycling counterpart by an order of magnitude.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In constrained data mining, user can specify constraints to prune the search space to avoid mining uninteresting knowledge. This is typically done by specifying some initial values of the constraints that are subsequently refinditeratively 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 talk, we will look at how we can 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 exploit the compressed database. Results from our extensive experimental study show that our proposed recycling algorithm outperform their non-recycling counterpart by an order of magnitude.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10112482
• 書誌情報: 情報処理学会研究報告データベースシステム（DBS）; 巻 2004, 号 72(2004-DBS-134), p. 395-395, 発行日 2004-07-14
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会