K-means clustering is a popular clustering algorithm based on the partition of data. However, K-means clustering algorithm suffers from some shortcomings, such as its requiring a user to give out the number of clusters at first, and its sensitiveness to initial conditions, and its being easily trapped into a local solution et cetera. The global Kmeans algorithm proposed by Likas et al is an inc...

In this paper, we present a novel algorithm for performing k-means clustering. It organizes all the patterns in a k-d tree structure such that one can find all the patterns which are closest to a given prototype efficiently. The main intuition behind our approach is as follows. All the prototypes are potential candidates for the closest prototype at the root level. However, for the children of ...

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...

Aiming at the problems of traditional K-means clustering algorithm, such as local optimal solution and slow speed caused by uncertainty k value randomness initial cluster center selection, this paper proposes an improved KMeans method. The algorithm first uses idea elbow rule based on sum squares errors to obtain appropriate number clusters k, then variance a measure degree dispersion samples, ...