CMDC：一种差异互补的迭代式多维度文本聚类算法

doi:10.11959/j.issn.1000-436x.2020152

Abstract

Abstract:

In response to the problems traditional multi-view document clustering methods separate the multi-view document representation from the clustering process and ignore the complementary characteristics of multi-view document clustering,an iterative algorithm for complementary multi-view document clustering——CMDC was proposed,in which the multi-view document clustering process and the multi-view feature adjustment were conducted in a mutually unified manner.In CMDC algorithm,complementary text documents were selected from the clustering results to aid adjusting the contribution of view features via learning a local measurement metric of each document view.The complementary text document of the results among the dimensionality clusters was selected by CMDC,and used to promote the feature tuning of the clusters.The partition consistency of the multi-dimensional document clustering was solved by the measure consistency of the dimensions.Experimental results show that CMDC effectively improves multi-dimensional clustering performance.

Key words: multi-view document clustering, complementary text, constrained document clustering, metric calculation

CLC Number:

TP301

Ruizhang HUANG,Ruina BAI,Yanping CHEN,Yongbin QIN,Xinyu CHENG,Youliang TIAN. CMDC:an iterative algorithm for complementary multi-view document clustering[J]. Journal on Communications, 2020, 41(8): 155-164.

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References 25

[1]	ALLAHYARI M , POURIYEH S , ASSEFI M ,et al. Text summarization techniques:a brief survey[J]. International Journal of Advanced Computer Science and Applications, 2017,8(10): 397-405.
[2]	QIAN M , ZHAI C . Unsupervised feature selection for multi-view clustering on text-image Web news data[C]// Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2014: 1963-1966.
[3]	YANG Y , WANG H . Multi-view clustering:a survey[J]. Big Data Mining and Analytics, 2018,1(2): 83-107.
[4]	BICKEL S , SCHEFFER T . Multi-view clustering[C]// Industrial Conference on Data Mining. Piscataway:IEEE Press, 2004: 19-26.
[5]	CHAUDHURI K , KAKADE S M , LIVESCU K ,et al. Multi-view clustering via canonical correlation analysis[C]// Proceedings of the 26th Annual International Conference on Machine Learning. New York:ACM Press, 2009: 129-136.
[6]	KUMAR A,DAUMé H , . A co-training approach for multi-view spectral clustering[C]// Proceedings of the 28th International Conference on Machine Learning. Washington:IMLS, 2011: 393-400.
[7]	KUMAR A , RAI P , DAUME H . Co-regularized multi-view spectral clustering[C]// Advances in Neural Information Processing Systems.[S.n.:s]. 2011: 1413-1421.
[8]	YIN Q , WU S , HE R ,et al. Multi-view clustering via pairwise sparse subspace representation[J]. Neurocomputing, 2015(156): 12-21.
[9]	TIAN F , GAO B , CUI Q ,et al. Learning deep representations for graph clustering[C]// 28th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2014: 1293-1299.
[10]	PENG X , XIAO S , FENG J ,et al. Deep subspace clustering with sparsity prior[C]// International Joint Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2016: 1925-1931.
[11]	CAI X , NIE F , HUANG H ,et al. Heterogeneous image feature integration via multi-modal spectral clustering[C]// Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2011: 1977-1984.
[12]	WANG Y , WU L . Beyond low-rank representations:orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering[J]. Neural Networks, 2018(103): 1-8.
[13]	XIE Y , LIN B , QU Y ,et al. Joint deep multi-view learning for image clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2020(99):1.
[14]	XIE J , GIRSHICK R , FARHADI A . Unsupervised deep embedding for clustering analysis[C]// International Conference on Machine Learning. New York:IMLS, 2016: 478-487.
[15]	PERKINS H , YANG Y . Dialog Intent induction with deep multi-view clustering[J]. arXiv Preprint,arXiv:1908.11487, 2019
[16]	XING E P , JORDAN M I , RUSSELL S J ,et al. Distance metric learning with application to clustering with side-information[C]// Advances in Neural Information Processing Systems.[S.n.:s.l]. 2003: 521-528.
[17]	YE J , ZHAO Z , LIU H . Adaptive distance metric learning for clustering[C]// 2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2007: 1-7.
[18]	BAGHSHAH M S , SHOURAKI S B . Kernel-based metric learning for semi-supervised clustering[J]. Neurocomputing, 2010,73(7-9): 1352-1361.
[19]	MOUTAFIS P , LENG M , KAKADIARIS I A . An overview and empirical comparison of distance metric learning methods[J]. IEEE Transactions on Cybernetics, 2016,47(3): 612-625.
[20]	HYUN Y , KIM N , CHO Y . A multi-dimensional issue clustering from the perspective consumers’ interests and R＆D[J]. Journal of the Korea Society of IT Services, 2015,14(1): 237-249.
[21]	黎万英, 黄瑞章, 丁志远 ,等. 基于用户行为特征的多维度文本聚类[J]. 计算机应用, 2018,38(11): 3127-3131.
	LI W Y , HUANG R Z , DING Z Y ,et al. Multi-dimensional text clustering with user behavior characteristics[J]. Journal of Computer Applications, 2018,38(11): 3127-3131.
[22]	DEVLIN J , CHANG M W , LEE K ,et al. BERT:pre-training of deep bidirectional transformers for language understanding[J]. arXiv Preprint,arXiv:1810.04805, 2018
[23]	BRBI? M , KOPRIVA I . Multi-view low-rank sparse subspace clustering[J]. Pattern Recognition, 2018(73): 247-258.
[24]	WANG X , LEI Z , GUO X ,et al. Multi-view subspace clustering with intactness-aware similarity[J]. Pattern Recognition, 2018(88): 50-63.
[25]	RASIWASIA N , MAHAJAN D , MAHADEVAN V ,et al. Cluster canonical correlation analysis[J]. Aistats, 2014: 823-831.

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数据集	样本个数/个	维度数量/维	类别数量/种
AMiner	1 500	2	3
MHN	2 605	3	4

算法	AMiner摘要维度	AMiner用户维度	MHN标题维度	MHN正文维度	MHN主题维度
k-means	0.679	0.710	0.525	0.825	0.709
MTCUBC	0.783	0.733	0.606	0.851	0.621
CMDC	0.818	0.850	0.668	0.837	0.756

算法	AMiner	MHN
Mv+k-means	0.782	0.760
P-MLRSSC	0.826	0.748
C-MLRSSC	0.833	0.817
MSC_IAS	0.769	0.854
CMDC	0.850	0.837

CMDC:an iterative algorithm for complementary multi-view document clustering

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