一种基于T-分布随机近邻嵌入的聚类集成方法

doi:10.11999/JEIT170937

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Abstract T-distributed Stochastic Neighbor Embedding (TSNE) is introduced into cluster ensemble problem and a cluster ensemble approach based on TSNE is proposed. First, TSNE is utilized to minimize Kullback-Leibler divergences between the high-dimensinal points corresponding to the rows of hypergraph’s adjacent matrix and the low-dimensional mapping points, which preserves the structure of high-dimensional space in low-dimensional space. Then, a hierarchical clustering algorithm is carried out in the low-dimensional space to obtain the final clustering result. Experimental results on several baseline datasets indicate that TSNE can improve the cluster results of hierarchical clustering algorithm and the proposed cluster ensemble method via TSNE outperforms state-of-the-art methods.

Key words： Machine learning Clustering analysis Cluster ensemble Hierarchical clustering

Received: 10 October 2017 Published: 10 April 2018

PACS:

TP391

Fund:The National Natural Science Foundation of China (61105057, 61375001), The Natural Science Foundation of Jiangsu Province (BK20151299), The Industry-Education-Research Prospective Project of Jiangsu Province (BY2016065-01)

Corresponding Authors: XU Sen E-mail: xusen@ycit.cn

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	XU Sen
	HUA Xiaopeng
	XU Jing
	XU Xiufang
	GAO Jun
	AN Jing

Cite this article:

XU Sen,HUA Xiaopeng,XU Jing等. Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding[J]. JEIT, 2018, 40(6): 1316-1322.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170937 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I6/1316

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