JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 48-53.doi: 10.6040/j.issn.1672-3961.0.2017.434

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Incremental multi-view clustering algorithm based on kernel K-means

ZHANG Peirui, YANG Yan*, XING Huanlai, YU Xiuying   

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
  • Received:2017-05-05 Online:2018-06-20 Published:2017-05-05

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Abstract: Because of the defect of long running time in the kernel based multi-view clustering algorithm(MVKKM)when dealing with large-scale datasets, the concept of incremental clustering model was introduced. The incremental multi-view clustering algorithm based on kernel K-means(IMVKKM)was proposed by combining MVKKM algorithm and incremental clustering framework. The dataset was divided into chunks and the MVKKM method was used in each data chunk to obtain a set of cluster centers,which was regarded as the initial cluster center of the next chunk. The cluster centers of all the chunks were combined and the final set of cluster result was identified by using MVKKM. The experimental results showed that IMVKKM algorithm had better clustering results and shorter running time than MVKKM algorithm on three large-scale datasets. The proposed approach could reduce the running time while keeping the clustering performance.

Key words: kernel function, multi-view kernel K-means, incremental clustering, cluster center, dataset chunk, multi-view clusterting

CLC Number: 

  • TP391
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