There are number of techniques proposed by several researchers to analyze the
performance of clustering algorithms in data mining. All these techniques are not suggesting …

Clustering is the popular unsupervised learning technique of data mining which divide the
data into groups having similar objects and used in various application areas. k-Means is …

Clustering is one of the data mining methods that partition large-sized data into subgroups
according to their similarities. K-means clustering algorithm works well in spherical or …

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 …

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 …

Data clustering is a process of arranging similar data into groups. A clustering algorithm
partitions a data set into several groups such that the similarity within a group is better than …

Clustering Performance is based iterative and analysis is a descriptive task that seeks to
identify homogeneous groups of objects based on the values of the methodology is search …

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 …