With the advent of modern techniques for scientific data collection, large quantities of data
are getting accumulated at various databases. Systematic data analysis methods are …

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 …

K-means is one of the most significant clustering algorithms in data mining. It performs well
in many cases, especially in the massive data sets. However, the result of clustering by K …

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 …

Clustering is a technique for dividing a set of similar objects into same groups and dissimilar
objects into different groups. Among different clustering algorithms, the K-means algorithm is …

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 …

K-means clustering algorithm which is a process of separating n number of points into K
clusters according to the predefined value of K is one of the clustering analysis algorithms …

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 …

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 is one of most prevalent data mining techniques that groups objects of same type.
The applications of clustering are enormous in fields like medical, business and education …