We propose a hybrid, unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization. We developed several heuristics to ...
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
This paper discusses a new type of semi-supervised document clustering that uses partial supervision to partition a large set of documents. Most clustering methods organizes docum...
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...