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 …

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 …

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 …

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 …

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 …

The set of objects having same characteristics are organized in groups and clusters of these
objects reformed known as Data Clustering. It is an unsupervised learning technique for …

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 …

K-means is a widely used partitional clustering method. While there are considerable
research efforts to characterize the key features of K-means clustering, further investigation …

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 …

Unsupervised learning is the process to partition the given data set into number of clusters
where similar data objects belongs same cluster and dissimilar data objects belongs to …