A hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression models for datasets. In the new formulation, (i) the performance function of the neural network regression models is modified such that the fuzzy clustering weightings can be introduced in these network models; (ii) the errors of these network models are feed-backed i...

Data clustering is useful in several areas such as machine learning, data mining, wireless sensor networks and pattern recognition. The most famous clustering approach is K-means which successfully has been utilized in numerous clustering problems, but this algorithm has some limitations such as local optimal convergence and initial point understanding. Clustering is the procedure of grouping o...

Clustering is a widely used technique of finding interesting patterns residing in the dataset that are not obviously known. The K-Means algorithm is the most commonly used partitioned clustering algorithm because it can be easily implemented and is the most efficient in terms of the execution time. However, due to its sensitiveness to initial partition it can only generate a local optimal solut...

Cluster analysis is a data mining technology designed to derive a good understanding of data to solve clustering problems by extracting useful information from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorithms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering and sorting behavior of ant col...

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...