Due to strong demand for the ability to enforce top-down structure on clustering results, semi-supervised clustering methods using pairwise constraints as side information have received increasing attention in recent years. However, most current methods are passive in the sense that the side information is provided beforehand and selected randomly. This may lead to the use of constraints that a...

Combinations of allele frequencies and pairwise linkage disequilibrium terms, each of which is permissible at the two-locus level, may not always be permissible at the three-locus level. These additional constraints on the possible maximum and minimum values for the pairwise disequilibrium terms are formally determined and numerically analyzed. In some cases, the three-locus constraints on a pa...

Feature selection is an important preprocessing step in mining high-dimensional data. Generally, supervised feature selection methods with supervision information are superior to unsupervised ones without supervision information. In the literature, nearly all existing supervised feature selection methods use class labels as supervision information. In this paper, we propose to use another form ...

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...