Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets

For target information systems containing both continuous and symbolic values, a novel attribute reduction method is proposed based on k-prototypes clustering and rough set theory under equivalent relations, which is suitable for hybrid data.Firstly, k-prototypes clustering is applied to obtain clus...

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Bibliographic Details
Published inJi suan ji ke xue Vol. 48; p. 342
Main Authors Li, Yan, Fan, Bin, Guo, Jie, Lin, Zi-Yuan, Zhao, Zhao
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.01.2021
Subjects
Algorithms
Classification
Clustering
Entropy (Information theory)
Equivalence
Information systems
Parameters
Prototypes
Rough set models
Set theory
Universe
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Summary:For target information systems containing both continuous and symbolic values, a novel attribute reduction method is proposed based on k-prototypes clustering and rough set theory under equivalent relations, which is suitable for hybrid data.Firstly, k-prototypes clustering is applied to obtain clusters of information systems by defining the distance of hybrid data, forming a division of the universe.Then the obtained clusters are used to replace equivalent classes in rough set theory, and the concepts of cluster-based approximate set, positive region, attribute reduction are correspondingly proposed.An attribute importance measure is also defined based on information entropy and the clusters.Finally, a variable precision positive-region reduction method is established, which can process both numerical and symbolic data, remove redundant attributes, reduce the needed storage and running time cost, and improve classification performance of classification algorithms.Besides, the division of different granularit
ISSN:1002-137X