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 …

The k-means algorithm is one of the most widely used methods to partition a dataset into
groups of patterns. However, most k-means methods require expensive distance …

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 method is a well-known clustering algorithm with an extensive range of
applications, such as biological classification, disease analysis, data mining, and image …

Clustering analysis plays an important role in scientific research and commercial
application. K-means algorithm is a widely used partition method in clustering. As the …

K-means clustering is a popular clustering algorithm based on the partition of data.
However, K-means clustering algorithm suffers from some shortcomings, such as its …

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

K-Means clustering algorithm attracts increasing focus in recent years. A pending problem of
K-Means clustering algorithm is that the performance is affected by the original cluster …

Data clustering is a process of arranging data into groups or a technique for classifying a
mountain of information into some manageable meaningful piles. The goal of clustering is to …

K-means clustering algorithm clusters datasets according to the certain clustering number k.
However, k cannot be confirmed beforehand. A new clustering validity index was designed …