Using String Kernel for Document Clustering | Zendy

Shi Qingwei | Zendy; Xiaodong Qiao | Zendy; Guangquan Xu | Zendy

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Using String Kernel for Document Clustering

Author(s) -

Shi Qingwei,

Xiaodong Qiao,

Guangquan Xu

Publication year - 2010

Publication title -

international journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2074-9015

pISSN - 2074-9007

DOI - 10.5815/ijitcs.2010.02.06

Subject(s) - computer science , string kernel , kernel (algebra) , cluster analysis , string (physics) , similarity (geometry) , document clustering , artificial intelligence , pattern recognition (psychology) , spectral clustering , kernel method , string metric , data mining , information retrieval , radial basis function kernel , string searching algorithm , mathematics , combinatorics , support vector machine , mathematical physics , image (mathematics) , pattern matching

In this paper, we present a string kernel based method for documents clustering. Documents are viewed as sequences of strings, and documents similarity is calculated by the kernel function. According to the documents similarity, spectral clustering algorithm is used to group documents. Experimental results shows that string kernel method outperform the standard k-means algorithm on the Reuters-21578 dataset.

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