K-means algorithm is very well-known in large data sets of clustering. This algorithm is
popular and more widely used for its easy implementation and fast working. However, it is …

Clustering analysis is the most significant step in data mining. This paper discusses the k-
means clustering algorithm and various distance functions used in k-means clustering …

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 …

Study of this paper describes the behavior of K-means algorithm. Through this paper we
have try to overcome the limitations of K-means algorithm by proposed algorithm. Basically …

The famous K-means clustering algorithm is sensitive to the selection of the initial centroids
and may converge to a local minimum of the criterion function value. A new algorithm for …

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 is a popular data analysis and data mining technique. K-Means is one of the most
popular data mining algorithms for being simple, scalable and easily modifiable to a variety …

Data clustering is an important data exploration technique with many applications in data
mining. The k-means algorithm is well known for its efficiency in clustering large data sets …

The most widely used algorithm in the cluster partitioning method is the K-Means algorithm.
Historically K-Means is still the best grouping algorithm among other grouping algorithms …

This paper proposes a K-means algorithm with the dynamic adjustable number of clusters.
The algorithm uses the improved Euclidean distance formula to calculate the distance …