K-means clustering algorithm and one of its enhancements are studied in this paper.
Clustering is the classification of objects into different groups, or more precisely, the …

Clustering analysis method is one of the main analytical methods in data mining, the method
of clustering algorithm will influence the clustering results directly. This paper discusses the …

The K-means algorithm is widely used because of its reliable theory, simple algorithm, fast
convergence and it can effectively handle large data sets. However, the traditional K-means …

Clustering analysis is one of the important problems in the fields of data mining and machine
learning. There are many different clustering methods. Among them, k-means clustering is …

In this paper we present a new clustering method based on K-means that have avoided
alternative randomness of initial center. This paper focused on K-means algorithm to the …

Clustering is the process of grouping similar data into a set of clusters. Cluster analysis is
one of the major data analysis techniques and k-means one of the most popular partitioning …

As a classic clustering method, the traditional K-Means algorithm has been widely used in
pattern recognition and machine learning. It is known that the performance of the K-means …

Data clustering is a frequently used technique in finance, computer science, and
engineering. In most of the applications, cluster sizes are either constrained to particular …

This paper presents a data clustering approach using modified K-Means algorithm based on
the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm …

The traditional k-means algorithm has sensitivity to the initial start center. To solve this
problem, this paper proposed a new method to find the initial center and improve the …