GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with many local optima. To circumvent this problem, we propose Niching Genetic K-means Algorithm (NGKA) that is based on modified deterministic crow...

Introduction CLUSTERING is a process of grouping a set of objects into clusters so that the objects in the same cluster have high similarity but are very dissimilar with objects in other clusters. The K-Means algorithm is well known for its efficiency in clustering large data sets. Fuzzy versions of the K-Means algorithm have been reported by Ruspini and Bezdek, where each pattern is allowed to...

Abstract The production of fish-based food processing has become a commodity for restaurants, catering and home consumption, but there are still many people who don’t know how fish can be processed in various dishes their daily needs. To find out to make dishes, the researchers provide solution cooking any kind food, starting from grouping types basic ingredients that must prepared, cook them, ...

This article gives a constant factor approximation algorithm for streaming k-means that usesO(k log n) space.

Data Analysis plays an indispensable role for understanding various phenomena. Clustering algorithms are a class of important tools for data analysis. K-means cluster analysis is considered to cluster protein variates across 3 species using SPSS 16.0. In this Paper we describe an approach to kmeans cluster analysis which grouped the sample data of the three species under study into four apriori...

K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on centroids clusters would be selected randomly. More suggestions have been given in order overcome this problem. Ensemble learning a method clusterin...