Conditional discriminative pattern mining: Concepts and algorithms

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• 作者：Zengyou He^a ; ^b ; Feiyang Gu^a ; Can Zhao^a ; Xiaoqing Liu^a ; Jun Wu^c ; Ju Wang ; ^d ; ^{wangju@uestc.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Discriminative pattern ; Emerging pattern ; Contrast sets ; Subgroup discovery ; Data mining
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：375
• 期：Complete
• 页码：1-15
• 全文大小：1468 K

文摘

Discriminative pattern mining is used to discover a set of significant patterns that occur with disproportionate frequencies in different class-labeled data sets. Although there are many algorithms that have been proposed, the redundancy issue that the discriminative power of many patterns mainly derives from their sub-patterns has not been resolved yet. In this paper, we consider a novel notion dubbed conditional discriminative pattern to address this issue. To mine conditional discriminative patterns, we propose an effective algorithm called CDPM (Conditional Discriminative Patterns Mining) to generate a set of non-redundant discriminative patterns. Experimental results on real data sets demonstrate that CDPM has very good performance on removing redundant patterns that are derived from significant sub-patterns so as to generate a concise set of meaningful discriminative patterns.