Emergence of modern techniques for scientific data collection has resulted in large scale
accumulation of data pertaining to diverse fields. Conventional database querying methods …

Clustering plays a very vital role in exploring data, creating predictions and to overcome the
anomalies in the data. Clusters that contain collateral, identical characteristics in a dataset …

Clustering is a challenging problem that is commonly used for many applications. It aims at
finding the similarity between data points and grouping similar ones into the same cluster. In …

The k-means algorithm is known to have a time complexity of O (n 2), where n is the input
data size. This quadratic complexity debars the algorithm from being effectively used in large …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

The k-means method aims to divide a set of N objects into k clusters, where each cluster is
represented by the mean value of its objects. This algorithm is simple and converges to local …

Clustering is one of the main task in datamining. It is useful to group and cluster the data.
There are a few ways to cluster the data such as partitional-based, hierarchical-based and …

In k-means clustering, we are given a set of n data points in d-dimensional space ℝ d and an
integer k and the problem is to determine a set of k points in ℝ d, called centers, so as to …

This paper presents an efficient algorithm, called pattern reduction (PR), for reducing the
computation time of k-means and k-means-based clustering algorithms. The proposed …

The k-means algorithm is one of the most widely used clustering algorithms and has been
applied in many fields of science and technology. One of the major problems of the k-means …