Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding

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

doi:10.11999/JEIT170937

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1316-1322

XU Sen, HUA Xiaopeng, XU Jing, XU Xiufang, GAO Jun, AN Jing. Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937

Citation:

XU Sen, HUA Xiaopeng, XU Jing, XU Xiufang, GAO Jun, AN Jing. Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937

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Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding

doi: 10.11999/JEIT170937

Funds:

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)

Received Date: 2017-10-10
Rev Recd Date: 2018-03-16
Publish Date: 2018-06-19

Abstract

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 hypergraphs 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.
- Machine learning,
- Clustering analysis,
- Cluster ensemble,
- Hierarchical clustering

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References(21)

References

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