The K-means algorithm has been a widely applied clustering technique, especially in the
area of marketing research. In spite of its popularity and ability to deal with large volumes of …

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 …

Mining knowledge from large amounts of spatial data is known as spatial data mining. It
becomes a highly demanding field because huge amounts of spatial data have been …

Clustering is a division of data into groups of similar objects. K-means has been used in
many clustering work because of the ease of the algorithm. Our main effort is to parallelize …

This paper surveys some historical issues related to the well-known k-means algorithm in
cluster analysis. It shows to which authors the different versions of this algorithm can be …

Performance of iterative clustering algorithms which converges to numerous local minima
depend highly on initial cluster centers. Generally initial cluster centers are selected …

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is
fundamental in knowledge acquisition. It is called Clustering in the literature and it is applied …

Clustering is one of the widely used knowledge discovery techniques to reveal structures in
a dataset that can be extremely useful to the analyst. In iterative clustering algorithms the …

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 …

Over the past five decades, k-means has become the clustering algorithm of choice in many
application domains primarily due to its simplicity, time/space efficiency, and invariance to …