This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringof-group-numbers encoding, we encode the prototypes of the clusters into th...
In this paper a fuzzy quantization dequantization criterion is used to propose an evaluation technique to determine the appropriate clustering algorithm suitable for a particular ...
A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolu...
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various cl...
Hye-Sung Yoon, Sang-Ho Lee, Sung-Bum Cho, Ju Han K...
Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the aut...