关键词: 半监督聚类, 成对约束, 核, 模糊聚类

Abstract: Clustering with constraints is an active area in machine learning and data mining.A semi-supervised mercer-kernel based fuzzy clustering algorithm with pairwise constraints and attributes weighted is proposed which incorporates both semi-
supervised learning technique and the kernel method into the traditional fuzzy clustering algorithm.The proposed algorithm performs clustering in high feature space mapped by a mercer kernels and considers the imbalance between the attributes fully.This algorithm experimentally outperforms a similar semi-supervised fuzzy clustering approach，i.e Pairwise Constrained Competitive Agglomeration（PCCA）.

Key words: semi-supervised clustering, pairwise constraints, kernel, fuzzy clustering