Clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. In this paper, a Cultural Algorithm (CA) is presented to optimize partition with N objects into K clusters. The CA is one of the effective methods for searching into the problem space in order to find a n...

Identifying software components is a crucial task in software development. There are a number of methods to identify components in the literature; however, the majority of these methods rely on clustering techniques with expert judgment. In contrast to the previous methods, which have used classical clustering techniques, this paper maps the components identification problem to an optimization ...

We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show th...

A method for statistical analysis of multimodal and/or highly distorted data is presented. The new methodology combines different clustering methods with the GAMLSS (generalized additive models location, scale, and shape) framework, therefore called c-GAMLSS, “clustering GAMLSS. ” In this extended structure, a latent variable (cluster) created to explain response-variable (target). Any all para...

Soft Computing models play an important role in the field of recognition, classification, data prediction, etc in various application fields. Soft Computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimization, Bacterial forging algotithm, classification and clustering, etc., the extraction of hidden information from large database is possible through the ...