We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based app...
Mohammad Al Hasan, Saeed Salem, Benjarath Pupacdi,...
One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optiml clustering for t...
Martin Hahmann, Peter Benjamin Volk, Frank Rosenth...
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...
Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) ...