NDLI: Semi-supervised clustering with pairwise and size constraints

Content Provider	IEEE Xplore Digital Library
Author	Shaohong Zhang Hau-San Wong Dongqing Xie
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Guangzhou Univ., Guangzhou, China (Shaohong Zhang; Dongqing Xie) \|\| Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China (Hau-San Wong)
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.
Starting Page	2450
Ending Page	2457
File Size	474332
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Cost function Partitioning algorithms Benchmark testing Educational institutions Data mining
Content Type	Text
Resource Type	Article

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