K-Means is one of the most popular partition based clustering technique. K-means has gain
popularity because of its simplicity and speed of classifying massive data rapidly and …

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

Amount of data is getting bigger in every moment and this data comes from everywhere;
social media, sensors, search engines, GPS signals, transaction records, satellites, financial …

The k-means method is a simple and fast clustering technique that exhibits the problem of
specifying the optimal number of clusters preliminarily. We address the problem of cluster …

This paper is a cluster analysis algorithm research carried out based on the existing data
mining, which focuses on the current popular and commonly used K-means algorithm, and …

This paper presents a new method for effectively selecting initial cluster centers in k-means
clustering. This method identifies the high density neighborhoods from the data first and then …

In the current world, there is a need to analyze and extract information from data. Clustering
is one such analytical method which involves the distribution of data into groups of identical …

Data mining has been defined as" The nontrivial extraction of implicit, previously unknown,
and potentially useful information from data". Clustering is the automated search for group of …

The k-means clustering algorithm is one of the most commonly used algorithms for
clustering analysis. The traditional k-means algorithm is, however, inefficient while working …