In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-s...
Bayesian Information Criterion (BIC) is a promising method for detecting the number of clusters. It is often used in model-based clustering in which a decisive first local maximum ...
In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We comb...
Dan Li, Jitender S. Deogun, William Spaulding, Bil...
We consider the topographic clustering task and focus on the problem of its evaluation, which enables to perform model selection: topographic clustering algorithms, from the origin...
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the d...