Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning Operations Research. However, a variety of situations we may have additional requirements or knowledge, distinct the underlying metric, regarding which pairs points should be clustered together. To capture analyze such scenarios, introduce novel family stochastic pairwise const...

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically derive useful constraints from data. In this paper, we experiment with two constraint-based correction approaches as p...

In recent years there has emerged the field of Constrained Clustering, which proposes clustering algorithms which are able to accommodate domain information to obtain a better final grouping. This information is usually provided as pairwise constraints, whose acquisition from humans can be costly. In this paper we propose a novel method based on word n-grams to automatically extract positive co...

This paper is concerned with some feasibility issues in mathematical programs with equilibrium constraints (MPECs) where additional joint constraints are present that must be satis ed by the state and design variables of the problems. We introduce su cient conditions that guarantee the feasibility of these MPECs. It turns out that these conditions also guarantee the feasibility of the quadratic...

All ecosystems are subjected to chronic disturbances, such as harvest, pollution, and climate change. The capacity to forecast how species respond to such press perturbations is limited by our imprecise knowledge of pairwise species interaction strengths and the many direct and indirect pathways along which perturbations can propagate between species. Network complexity (size and connectance) h...

Recent chromosome conformation capture (3C) experiments produce pairwise interactions between fragments of DNA that are spatially close in the nucleus of a cell. Visualizing 3C data on the scale of the whole genome allows scientists to gain insight into chromosomal packing in the nucleus of a cell and to generate hypotheses about what kinds of genomic features (e.g. gene locations, DNA accessib...

Topic models such as Latent Dirichlet Allocation (LDA) can discover topics from a large collection of documents in an unsupervised fashion and thus is one of the most popular text analysis tool currently in use. However, when using it in practice, the topics discovered by topic model don’t always make sense to end users. The poor quality topics will substantially undermine a topic model system’...

Resolving the semantic heterogeneity in the semantic web requires finding correspondences between ontologies describing resources. In particular, with the explosive growth of data sets in the Linked Open Data, linking multiple vocabularies and ontologies simultaneously, known as holistic matching problem, becomes necessary. Currently, most state-of-the-art matching approaches are limited to pai...

Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user’s expectations. To make such a semi-supervised categorization approach acceptable for the user, thi...