K-means algorithm is a very popular clustering algorithm which is famous for its simplicity.
Distance measure plays a very important rule on the performance of this algorithm. We have …

Clustering analysis is the most significant step in data mining. This paper discusses the k-
means clustering algorithm and various distance functions used in k-means clustering …

Clustering is the process of grouping the data based on their similar properties. Meanwhile,
it is the categorization of a set of data into similar groups (clusters), and the elements in each …

This paper presents a new approach to overcome one of the most known disadvantages of
the well-known Kmeans clustering algorithm. The problems of classical Kmeans are such as …

In k-means clustering, we are given a set of n data points in d-dimensional space ℝ d and an
integer k and the problem is to determine a set of k points in ℝ d, called centers, so as to …

K-means clustering algorithm clusters datasets according to the certain clustering number k.
However, k cannot be confirmed beforehand. A new clustering validity index was designed …

In this paper, the effect of the initial clustering center selection on the performance of the K-
means algorithm is studied, and the performance of the algorithm is enhanced through …

Emergence of modern techniques for scientific data collection has resulted in large scale
accumulation of data pertaining to diverse fields. Conventional database querying methods …

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 …

Abstract k-means is one of the most widely used partition based clustering algorithm. But the
initial centroids generated randomly by the k-means algorithm cause the algorithm to …