A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...
Farial Shahnaz, Michael W. Berry, V. Paul Pauca, R...
As technology advances we encounter more available data on moving objects, which can be mined to our benefit. In order to efficiently mine this large amount of data we propose an ...
We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for kpartite maximal cliques. Unlike previous methods, CLICKS mines subs...
Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorith...
Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-b...