K-means is one of ten popular clustering algorithms. However, k-means performs poorly due
to the presence of outliers in real datasets. Besides, a different distance metric makes a …

Clustering technique is mainly focus on pattern recognition for further organizational design
analysis which finds groups of data objects such that objects in a group are similar to one …

The k-Means clustering algorithm is an old algorithm that has been intensely researched
owing to its ease and simplicity of implementation. Clustering algorithm has a broad …

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 …

An improved version of K‐means clustering algorithm that can be applied to big data
through lower processing loads with acceptable precision rates is presented here. In this …

Clustering is the process of grouping the data based on their similar properties. Meanwhile,
it is the categorization of a set of data into similar groups (clusters), and the elements in each …

K-means is an effective clustering technique used to separate similar data into groups based
on initial centroids of clusters. In this paper, Normalization based K-means clustering …

The classical k-means clustering algorithm is easily misled by outliers. To address this
problem, we modify its centroid update step so that outliers are avoided when new centroids …

In the field of data mining, clustering is a technique where millions of data points are
grouped together to form a cluster. Data of same class are grouped together. K-Means …

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 …