Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
A critical problem in clustering research is the definition of a proper metric to measure distances between points. Semi-supervised clustering uses the information provided by the ...
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
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...
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) ...
Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such...