The K-means clustering algorithm is an old algorithm that has been intensely researched owing to its simplicity of implementation. However, there have also been criticisms on its performance, in particular, for demanding the value of K a priori. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clus...

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...

The indices such as h-index, e-index etc has received much attention from the scientific community owing to their prowess to impact journal quality. Many different indicatorshave been developed to overcome drawbacks of h-index. Nearly four indices which are of prime importance in the publishing industry were utilized.In this paper, we present a modified k-means algorithm to generate three clust...

Clustering is used to identify the relationship among different objects from large volume of data. The clustering analysis is feasible only when the groups are formed with important features. The existing K-Means clustering processing time and the computation cost is high. The proposed two level variable weighting algorithm calculates weights for both views and variables to identify the importa...

This chapter presents an improved method called the “Adaptive Interbeat Interval Analysis (AIIA) method”. The AIIA method uses the Simple K-Means algorithm for symbolization, which offers a new way to represent subtle variations between two interbeat intervals without human intervention. After symbolization, it uses the n-gram algorithm to generate different kinds of symbolic sequences. Each sy...

Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.

October 21, 2009 Type Package Title Spherical k-Means Clustering Version 0.1-2 Author Kurt Hornik, Ingo Feinerer, Martin Kober Maintainer Kurt Hornik Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a simple fixed-point algorithm and an interface to the CLUTO vcluster program. License GPL-2 Imports slam...