We present a novel unsupervised learning scheme that simultaneously clusters variables of several types (e.g., documents, words and authors) based on pairwise interactions between...
Abstract. Clustering is often considered the most important unsupervised learning problem and several clustering algorithms have been proposed over the years. Many of these algorit...
Abstract. Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general pr...
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
Organizing Web search results into a hierarchy of topics and subtopics facilitates browsing the collection and locating results of interest. In this paper, we propose a new hierar...