Fuzzy-clustering methods, such as fuzzy k-means and Expectation Maximization, allow an object to be assigned to multiple clusters with different degrees of membership. However, th...
Abstract. The goal of this article is to introduce a collaborative clustering approach to the domain of ubiquitous knowledge discovery. This clustering approach is suitable in peer...
Abstract. Scientists’ ability to generate and collect massive-scale datasets is increasing. As a result, constraints in data analysis capability rather than limitations in the av...
YongChul Kwon, Dylan Nunley, Jeffrey P. Gardner, M...
We present an Immune Inspired Algorithm, based on CLONALG, for software test data evolution. Generated tests are evaluated using the mutation testing adequacy criteria, and used to...
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...