Conditional discriminative pattern mining: Concepts and algorithms
Autor: | Can Zhao, Zengyou He, Jun Wu, Xiaoqing Liu, Feiyang Gu, Ju Wang |
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Rok vydání: | 2017 |
Předmět: |
Information Systems and Management
Discriminative pattern mining business.industry Pattern recognition 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Theoretical Computer Science Effective algorithm Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Discriminative model Artificial Intelligence Control and Systems Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) 020201 artificial intelligence & image processing Artificial intelligence business computer Software Mathematics |
Zdroj: | Information Sciences. 375:1-15 |
ISSN: | 0020-0255 |
DOI: | 10.1016/j.ins.2016.09.047 |
Popis: | 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. |
Databáze: | OpenAIRE |
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