The k-means algorithm is one of the most widely used methods to partition a dataset into
groups of patterns. However, most k-means methods require expensive distance …

Data clustering is a process of arranging data into groups or a technique for classifying a
mountain of information into some manageable meaningful piles. The goal of clustering is to …

Clustering analysis plays an important role in scientific research and commercial
application. K-means algorithm is a widely used partition method in clustering. As the …

The main problems of K-means algorithm which is a basic algorithm in clustering are
outlined in this paper, such as determination of the optimal clusters, selection of initial …

The k-means clustering algorithm is one of the most commonly used algorithms for
clustering analysis. The traditional k-means algorithm is, however, inefficient while working …

This paper describes a new $ k $-means type clustering algorithm which gives excellent
results for a moderate computational cost. It is particularly suitable for partitioning large data …

In this paper, we present a novel algorithm for performing k-means clustering. It organizes all
the patterns in a kd tree structure such that one can find all the patterns which are closest to …

In k-means clustering algorithm, the number of centroids is equal to the number of the
clusters in which data has to be partitioned which in turn is taken as an input parameter. The …

Clustering problems arise in many different applications: machine learning, data mining,
knowledge discovery, data compression, vector quantization, pattern recognition and pattern …

The K-means method is a well-known clustering algorithm with an extensive range of
applications, such as biological classification, disease analysis, data mining, and image …