Document clustering method using dimension reduction and support vector clustering to overcome sparseness

Expert Systems with Applications

Volumn 41, Issue 7, 2014, Pages 3204-3212

  Jun, Sunghae ^a   Park, Sang Sung ^b   Jang, Dong Sik ^b  

^a Chongju University   (South Korea)

^b Korea University   (South Korea)

Author keywords

Dimension reduction; Document clustering; K means clustering based on support vector clustering; Patent clustering; Silhouette measure; Sparseness problem

Indexed keywords

DIMENSION REDUCTION; DOCUMENT CLUSTERING; PATENT CLUSTERING; SILHOUETTE MEASURE; SPARSENESS PROBLEM; SUPPORT VECTOR CLUSTERING;

DATA MINING; INFORMATION RETRIEVAL; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PATENTS AND INVENTIONS; TECHNOLOGICAL FORECASTING;

CLUSTER ANALYSIS;

EID: 84890497754     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.11.018     Document Type: Article

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