Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves performance, with respect to the true data labels. However, in most of these experiments, results are averaged over different randomly chosen constraint sets, thereby masking interesting properties of individual...

Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering methods have become very popular. These methods began with incorporating pairwise constraints and have developed into more general methods that can learn appropriate distance metrics. However, s...

To identify modules of interacting molecules often gene expression is analyzed with clustering methods. Constrained or semi-supervised clustering provides a framework to augment the primary, gene expression data with secondary data, to arrive at biological meaningful clusters. Here, we present an approach using constrained clustering and present favorable results on a biological data set of gen...

The RFMmodel provides an effective measure for customers’ consumption behavior analysis, where three variables, namely, consumption interval, frequency, and money amount are used to quantify a customer’s loyalty and contribution. Based on the RFM value, customers can be clustered into different groups and the group information is very useful in market decision making. However, most previous wor...

Clustering is one of most important methods of data mining. It is used to identify unknown yet interesting and useful patterns or trends in datasets. There are different types of clustering algorithms such as partitioning, hierarchical, grid and density-based. In general, clustering methods are considered unsupervised, however, in recent years the new branch of clustering algorithms has emerged...

A new constrained model is discussed as a way of incorporating efficiently a priori expert knowledge into a clustering problem of a given individual set. The first innovation is the combination of fusion constraints, which request some individuals to belong to one cluster, with exclusion constraints, which separate some individuals in different clusters. This situation implies to check the exis...

Submitted Abstract We use column generation and a specialized branching technique for solving constrained clustering problems. We also develop and implement an innovative com-binatorial method for solving the pricing subproblems. Computational experiments comparing the resulting branch-and-price method to competing methodologies in the literature are presented and suggest that our technique yie...

Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance in the analysis of gene expression data. This data provides a mean to begin elucidating the large-scale modular organization of the cell. The authors consider the challenging task of developing exploratory analytical ...

This paper describes an interactive tool for constrained clustering that helps users to select effective constraints efficiently during the constrained clustering process. This tool has some functions such as 2-D visual arrangement of a data set and constraint assignment by mouse manipulation. Moreover, it can execute distance metric learning and k-medoids clustering. In this paper, we show the...

In this work, we study constrained clustering, where some constraints are utilized to guide the clustering process. In existing work on this topic, two main categories of constraints have been explored, namely pairwise and cardinality constraints. Pairwise constraints enforce that the cluster labels of two instances be the same (must-link constraints) or different (cannot-link constraints). Car...