Semi-supervised clustering with pairwise and size constraints

Shaohong Zhang, Hau-San Wong, Dongqing Xie

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

8 Citations (Scopus)

Abstract

In recent years, semi-supervised clustering receives considerable attention in the pattern recognition and data mining communities. This type of clustering algorithms takes advantage of partial prior knowledge, and significant improved performance beyond traditional unsupervised clustering algorithms is observed. In general, the partial prior knowledge is mainly in the form of pairwise constraints, which specify whether point pairs should be in the same cluster or in different clusters. Moreover, some other forms of constraints also attract research interests, for example, the balance constraint or the size constraint. However, it is also important to consider different types of constraints simultaneously, since different types of prior knowledge might have their own bias when considered separately. In this paper, we propose an improved algorithm to incorporate the pairwise and size constraints into a unified framework. Experiments on several benchmark data sets demonstrate that the proposed unified algorithm outperforms previous approaches under a variety of different conditions, which demonstrates that judicious integration of different types of constraints can result in improved performance than in those cases where only a single kind of constraint is used.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages2450-2457
ISBN (Print)9781479914845
DOIs
Publication statusPublished - 3 Sept 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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