Ensemble subspace clustering of text data using two-level features

	Ensemble subspace clustering of text data using two-level features
	He Zhao; Salman Salloum; Yeshou Cai; Joshua Zhexue Huang
刊名	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
	2017
文献子类	期刊论文
英文摘要	This paper proposes a new integrated method for ensemble subspace clustering of high dimensional sparse text data. Our method employs two-level feature representation of text data (words and topics) to generate clusters from subspaces. We also use ensemble clustering to increase the robustness of the clusters. This method depends on topic modeling to get the two-level feature representation of text data and to generate different ensemble components. By using both topics and words to cluster text data, we can get more interpretable clusters as we can measure the weight of words and topics in each cluster. In order to evaluate the proposed method, we have conducted several experiments on seven real-life data sets. While some of these data sets are easy to cluster, others are hard, and some others contain unbalanced data. Experimental results on this diversity of data sets show that our method outperforms other methods for ensemble clustering.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12568]
专题	深圳先进技术研究院_数字所
作者单位	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
推荐引用方式 GB/T 7714	He Zhao,Salman Salloum,Yeshou Cai,et al. Ensemble subspace clustering of text data using two-level features[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2017.
APA	He Zhao,Salman Salloum,Yeshou Cai,&Joshua Zhexue Huang.(2017).Ensemble subspace clustering of text data using two-level features.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS.
MLA	He Zhao,et al."Ensemble subspace clustering of text data using two-level features".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2017).