Open Access Open Access  Restricted Access Subscription or Fee Access

Text Clustering Based on Kernel KNN Clustering Algorithm

Hao Xiong, Sheng Sun, Yunfang Feng


Document clustering is a popular tool for automatically organizing a large collection of documents. In this paper, we propose a Kernel-based K-Nearest Neighbor (KKNNC) clustering algorithm based on the KNN method. Our algorithm maps samples into a higher-dimensional feature space using a nonlinear function before clustering, then in kernel space divides them linearly. We also propose a new attribute selection method—ATS algorithm, which can select important terms in documents. Our algorithm first uses ATS to eliminate redundant attributes in data sets, then gives each of the selective attributes a weight value according to the relationship between these attributes. The experimental results show that our algorithm is effective in the text clustering task.


k-nearest neighbor, kernel method, text clustering

Full Text:



  • There are currently no refbacks.

Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.