Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …

The rise of emerging cyberthreats has led to a shift of focus on identifying the source of
threat instead of the type of attack to provide a more effective defense to compromised …

To deal with the uncertainty, vagueness and overlapping distribution within the data sets, a
novel incremental fuzzy cluster ensemble method based on rough set theory (IFCERS) is …

Semi-supervised cluster ensemble usually introduces a small amount of supervision in the
first stage of cluster ensemble, ie, ensemble generation, by performing many runs of semi …

Clustering ensemble, also referred to as consensus clustering, has emerged as a method of
combining an ensemble of different clusterings to derive a final clustering that is of better …

Cluster ensembles have emerged as a powerful tool to obtain clusters of data points by
combining a library of clustering solutions into a consensus solution. In this paper, we …

Genes act in concert with each other in specific contexts to perform their functions.
Determining how these genes influence complex traits requires a mechanistic …

The Minkowski weighted K-means (MWK-means) is a recently developed clustering
algorithm capable of computing feature weights. The cluster-specific weights in MWK-means …

Ensemble clustering is an important approach in machine learning, which combines multiple
hypotheses to minimize the risk of selecting a wrong hypothesis or local minimum. In this …