Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

A clustering ensemble aims to combine multiple clustering models to produce a better result
than that of the individual clustering algorithms in terms of consistency and quality. In this …

In this paper, we propose a new ensemble clustering approach termed ensemble clustering
using factor graph (ECFG). Compared to the existing approaches, our approach has three …

Clustering analysis is a significant research topic in discovering cancer using different
profiles of gene expression, which is very important to successfully diagnose and treat the …

For obtaining the more robust, novel, stable, and consistent clustering result, clustering
ensemble has been emerged. There are two approaches in clustering ensemble …

The ensemble clustering tries to aggregate a number of basic clusterings with the aim of
producing a more consistent, robust and well-performing consensus clustering result. The …

In wireless sensor networks, clustering is said to be the most noteworthy technique for
increasing the lifetime of network that directly leads a better routing mechanism. This …

Cluster ensemble is one of the main branches in the ensemble learning area which is an
important research focus in recent years. The objective of cluster ensemble is to combine …

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …

Performing clustering analysis is one of the important research topics in cancer discovery
using gene expression profiles, which is crucial in facilitating the successful diagnosis and …