This paper presents a comprehensive review of existing techniques of k-means clustering
algorithms made at various times. The kmeans algorithm is aimed at partitioning objects or …

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 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 …

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 …

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 …

In statistic and data mining, k-means is well known for its efficiency in clustering large data
sets. The aim is to group data points into clusters such that similar items are lumped together …

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 …

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 …

Cluster analysis method is one of the most analytical methods of data mining. The method
will directly influence the result of clustering. This paper discusses the standard of k-mean …

In the field of data mining, clustering is a technique where millions of data points are
grouped together to form a cluster. Data of same class are grouped together. K-Means …