Structure Identification-Based Clustering According to Density Consistency | Zendy

Chunzhong Li | Zendy; Zongben Xu | Zendy

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Structure Identification-Based Clustering According to Density Consistency

Author(s) -

Chunzhong Li,

Zongben Xu

Publication year - 2011

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2011/890901

Subject(s) - cluster analysis , consistency (knowledge bases) , outlier , data mining , pattern recognition (psychology) , single linkage clustering , data set , computer science , identification (biology) , dimension (graph theory) , set (abstract data type) , cluster (spacecraft) , feature (linguistics) , artificial intelligence , cure data clustering algorithm , clustering high dimensional data , process (computing) , algorithm , mathematics , correlation clustering , combinatorics , linguistics , philosophy , botany , biology , programming language , operating system

Structure of data set is of critical importance in identifying clusters, especiallythe density difference feature. In this paper, we present a clustering algorithmbased on density consistency, which is a filtering process to identify samestructure feature and classify them into same cluster. This method is notrestricted by the shapes and high dimension data set, and meanwhile it isrobust to noises and outliers. Extensive experiments on synthetic and realworld data sets validate the proposed the new clustering algorithm

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