Semi-supervised clustering with discriminative random fields

被引:14
作者
Chang, Chin-Chun [1 ]
Chen, Hsin-Yi [1 ]
机构
[1] Natl Taiwan Ocean Univ, Dept Comp Sci & Engn, Keelung 202, Taiwan
关键词
Semi-supervised clustering; Discriminative random fields; GEOMETRICAL STRUCTURE; METRIC ADAPTATION; CONSTRAINTS; OPTIMIZATION;
D O I
10.1016/j.patcog.2012.05.021
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Semi-supervised clustering exploits a small quantity of supervised information to improve the accuracy of data clustering. In this paper, a framework for semi-supervised clustering is proposed. This framework is capable of integrating with a traditional clustering algorithm seamlessly, and particularly useful for the application where a traditional clustering is designated to use. In the proposed framework, discriminative random fields (DRFs) are employed to model the consistency between the result of a traditional clustering algorithm and the supervised information with the assumption of semi-supervised learning. The semi-supervised clustering problem is thus formulated as finding the label configuration with the maximum a posteriori (MAP) probability of the DRF. A procedure based on the iterated conditional modes algorithm and a metric-learning algorithm is developed to find a suboptimal MAP solution of the DRF. The proposed approach has been tested against various data sets. Experimental results demonstrate that our approach can enhance the clustering accuracy, and thus prove the feasibility of the proposed approach. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:4402 / 4413
页数:12
相关论文
共 62 条
[1]
[Anonymous], 2004, ICML
[2]
[Anonymous], 2006, BOOK REV IEEE T NEUR
[3]
[Anonymous], 2003, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
[4]
[Anonymous], 2004, REV MACH LEARN TECH
[5]
[Anonymous], 2004, P 10 ACM SIGKDD INT, DOI DOI 10.1145/1014052.1014062
[6]
[Anonymous], 2002, NIPS
[7]
[Anonymous], 2007, Uci machine learning repository
[8]
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms
[9]
[Anonymous], 2001, PROC 18 INT C MACH L
[10]
Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data [J].
Baghshah, Mahdieh Soleymani ;
Shouraki, Saeed Bagheri .
PATTERN RECOGNITION, 2010, 43 (08) :2982-2992