Clustering is the unsupervised method of grouping data samples to form a partition of a given dataset. Such grouping is typically done based on homogeneity assumptions of clusters over an attribute space and hence the precise definition of the similarity metric affects the clusters inferred. In recent years, new formulations of clustering have emerged that posit indirect constraints on clusteri...

Abstract Statistical clustering criteria with free scale parameters and unknown cluster sizes are inclined to create small, spurious clusters. To mitigate this tendency a statistical model for cardinality–constrained clustering of data with gross outliers is established, its maximum likelihood and maximum a posteriori clustering criteria are derived, and their consistency and robustness are ana...

We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clusteri...

It is well-known that outliers and noisy data can be very harmful when applying clustering methods. Several fuzzy clustering methods which are able to handle the presence of noise have been proposed. In this work, we propose a robust clustering approach called F-TCLUST based on an “impartial” (i.e., self-determined by data) trimming. The proposed approach considers an eigenvalue ratio constrain...

Clusterings of graphs are often constructed and evaluated with the aid of a quality measure. Numerous such measures exist, some of which adapt an established measure for graph cuts to clusterings. In this work we pursue the problem of finding clusterings which simultaneously feature guaranteed intraand good intercluster quality. To this end we systematically assemble a range of cut-based bicrit...

Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the local correlation structures: associate each cluster with an independent weighting vector and embed it in the subspace spanned by an adaptive combination of the dimensions. Our clustering algorithm takes advantage of the...

Abstract: In this work clustering performance has been increased by proposes an algorithm called constrained informationtheoretic co-clustering (CITCC). In this work mainly focus on co-clustering and constrained clustering. Co-clustering method is differing from clustering methods it examine both document and word at a same time. A novel constrained coclustering approach proposed that automatic...

This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Kohonen maps, elastic nets and fuzzy c-means). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clus...

The application of “concentration” steps is the main principle behind Forgy’s kmeans algorithm and Rousseeuw and van Driessen’s fast-MCD algorithm. Although they share this principle, it is not completely straightforward to combine both algorithms for developing a clustering method which is not affected by a certain proportion of outlying observations and that is able to cope with non spherical...