Constrained Clustering with Imperfect Oracles
Publication in refereed journal


摘要While clustering is usually an unsupervised operation, there are circumstances where we have access to prior belief that pairs of samples should (or should not) be assigned with the same cluster. Constrained clustering aims to exploit this prior belief as constraint (or weak supervision) to influence the cluster formation so as to obtain a data structure more closely resembling human perception. Two important issues remain open: 1) how to exploit sparse constraints effectively and 2) how to handle ill-conditioned/noisy constraints generated by imperfect oracles. In this paper, we present a novel pairwise similarity measure framework to address the above issues. Specifically, in contrast to existing constrained clustering approaches that blindly rely on all features for constraint propagation, our approach searches for neighborhoods driven by discriminative feature selection for more effective constraint diffusion. Crucially, we formulate a novel approach to handling the noisy constraint problem, which has been unrealistically ignored in the constrained clustering literature. Extensive comparative results show that our method is superior to the state-of-the-art constrained clustering approaches and can generally benefit existing pairwise similarity-based data clustering algorithms, such as spectral clustering and affinity propagation.
著者Zhu X., Loy C.C., Gong S.
期刊名稱IEEE Transactions on Neural Networks and Learning Systems
出版社IEEE Computational Intelligence Society
出版地United States
頁次1345 - 1357
關鍵詞Affinity propagation, constrained clustering, constraint propagation, feature selection, imperfect oracles, noisy constraints, similarity/distance measure, Spectral clustering (SPClust)

上次更新時間 2020-22-10 於 02:30