Clustering with Constraints

Wagstaff, Kiri L.; Davidson, Ian; Pensa, Ruggero G.

doi:10.1201/9781584889977.ch5

Cited by 31 publications

(53 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea of considering domain knowledge as related/unrelated pairs in this paper is analogous to the notions of must/cannot links in semi-supervised clustering [3,39,40]. However, in semi-supervised clustering, the labeled data is used for cluster initialization [3] and the link constraints must be satisfied [39,40].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Communities in Social Networks using Max-Min Modularity

Chen¹,

Zaı̈ane²,

Goebel³

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure, which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are already known, our method shows improvement over previous algorithms. In addition, when applied to randomly generated networks for which we only have approximate information about communities, it gives promising results which shows the algorithm's robustness against noise.

show abstract

Section: Related Workmentioning

confidence: 99%

“…However, in semi-supervised clustering, the labeled data is used for cluster initialization [3] and the link constraints must be satisfied [39,40]. Moreover, the number of clusters k is usually required as the starting parameter.…”

Section: Related Workmentioning

confidence: 99%

Detecting Communities in Social Networks using Max-Min Modularity

Chen¹,

Zaı̈ane²,

Goebel³

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…1 (Wagstaff & Cardie, 2000) and (Wagstaff, Cardie, Rogers, & Schroedl, 2001) first used such pairwise information for semi-supervised clustering tasks by modifying the standard k-means clustering algorithm to take into account the pairwise similarity and dissimilarity constraints. Extensions have also been made to model-based clustering based on the expectation-maximization (EM) algorithm for Gaussian mixture models (Shental, Bar-Hillel, Hertz, & Weinshall, 2004;Lu & Leen, 2005).…”

Section: Semi-supervised Metric Learning Based On Pairwise Constraintsmentioning

confidence: 99%

A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability

2008

View full text Add to dashboard Cite

In recent years, metric learning in the semi-supervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

show abstract

“…Clustering techniques where the external input is provided by constraints are referred to as constrained clustering. These techniques typically consider two types of constraints: must-link constraints, which specify that two samples should be in the same cluster, and cannot-link constraints, which specify that two samples must be in different clusters [Wagstaff and Cardie 2000]. These constraints have been incorporated into many clustering algorithms, such as K-means clustering [Wagstaff and Cardie 2000], hierarchical clustering [Klein et al 2002], and clustering with Gaussian mixture models [Shental et al 2004].…”

Section: Background and Related Workmentioning

confidence: 99%

“…These techniques typically consider two types of constraints: must-link constraints, which specify that two samples should be in the same cluster, and cannot-link constraints, which specify that two samples must be in different clusters [Wagstaff and Cardie 2000]. These constraints have been incorporated into many clustering algorithms, such as K-means clustering [Wagstaff and Cardie 2000], hierarchical clustering [Klein et al 2002], and clustering with Gaussian mixture models [Shental et al 2004]. Since spectral clustering has gained much popularity in recent years [Shi and Malik 2000], adapting constraints to spectral clustering has also attracted considerable attention in the literature.…”

Section: Background and Related Workmentioning

confidence: 99%

Active co-analysis of a set of shapes

Wang¹,

et al. 2012

View full text Add to dashboard Cite

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the usergiven set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.

show abstract

Clustering with Constraints

Cited by 31 publications

References 0 publications

Detecting Communities in Social Networks using Max-Min Modularity

Detecting Communities in Social Networks using Max-Min Modularity

A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability

Active co-analysis of a set of shapes

Contact Info

Product

Resources

About