Semi-supervised kernel-based fuzzy C-means with pairwise constraints

Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based fuzzy term defined by the violation of constraints is included. The proposed PCKFCM is compared with other clustering techniques on benchmark and the experimental results convince that effective use of constraints improves the performance of kernel-based clustering. As for the effect of key parameter selection and the non-linear capability, it outperforms a similar semi-supervised fuzzy clustering approach Pairwise Constrained Competitive Agglomeration (PCCA).

[1]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[2]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[3]  John Hallam,et al.  IEEE International Joint Conference on Neural Networks , 2005 .

[4]  Inderjit S. Dhillon,et al.  Semi-supervised graph clustering: a kernel approach , 2005, Machine Learning.

[5]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[6]  Arindam Banerjee,et al.  Active Semi-Supervision for Pairwise Constrained Clustering , 2004, SDM.

[7]  Arindam Banerjee,et al.  Semi-supervised Clustering by Seeding , 2002, ICML.

[8]  R. Mooney,et al.  Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering , 2003 .

[9]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[10]  Song-can Chen,et al.  Kernel-based fuzzy and possibilistic c-means clustering , 2003 .

[11]  Daoqiang Zhang,et al.  Semi-supervised Kernel-Based Fuzzy C-Means , 2004, ICONIP.

[12]  Raymond J. Mooney,et al.  Integrating constraints and metric learning in semi-supervised clustering , 2004, ICML.

[13]  Carlotta Domeniconi,et al.  An Adaptive Kernel Method for Semi-supervised Clustering , 2006, ECML.

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  Nozha Boujemaa,et al.  Semi-Supervised Fuzzy Clustering with Pairwise-Constrained Competitive Agglomeration , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..