Cluster analysis is a descriptive task that seek to identify homogenous group of object and it
is also one of the main analytical method in data mining. K-mean is the most popular …

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 …

The importance of unsupervised clustering methods is well established in the statistics and
machine learning literature. Many sophisticated unsupervised classification techniques have …

In the field of data mining, the approach of assigning a set of items to one similar class called
cluster and the process termed as Clustering. Document clustering is one of the rapidly …

In most of industrial processes, there are mainly two issues: 1. working models will be
different at different time (multi-models); 2. different variables have different sampling …

The K-means algorithm is one of the most popular algorithms in Data Science, and it is
aimed to discover similarities among the elements belonging to large datasets, partitioning …

Abstract Wireless Sensor Networks (WSN) can be considered as a self-organizing system
that might be a good alternative to wired systems due to their ease of deployment in distant …

Clustering is one of the important unsupervised learning in data mining to group the similar
features. The growing point of the cluster is known as a seed. To select the appropriate seed …

Data reduction in storage systems is an effective solution to minimize the management cost
of a data center. To maximize data-reduction efficiency, prior works propose post …

Data mining techniques has been used as a recent trend for achieving diagnostics results, in
medical fields such as kidney dialysis. Data mining concepts are used to examine a rich …