Clustering is an unsupervised classification method that focused on grouping data into
clusters. The objects in each cluster are very similar but different from the objects in the other …

Support Vector Machine (SVM) is an effective model for many classification problems.
However, SVM needs the solution of a quadratic program which require specialized code. In …

The Cluster analysis is a major technique for statistical analysis, machine learning, pattern
recognition, data mining, image analysis and bioinformatics. K-means algorithm is one of the …

Clustering is regarded as a process that organize objects into groups where members are
similar and the process help in arranging objects and finding similar patterns. The main idea …

In this world, each and every activity is enriched with lot of information. Business and other
organization needs information for better decision making. Business Intelligence is a set of …

Clustering is a grouping technique that has long been used to relate data homogeneously.
With the huge growth of complex datasets from different sources in the last decade, new …

Evolutionary algorithms have become more popular nowadays in order to solve non-linear
complex real world problems. One of these many applications includes clustering of …

The precipitation patterns of seventeen locations in Bangladesh from 1961 to 2014 were
studied using a cluster analysis and metric multidimensional scaling. In doing so, the current …

Purpose The purpose of this paper is to introduce an evaluation methodology for employee
profiles that will provide feedback to the training decision makers. Employee profiles play a …

Principal component analysis (PCA) guided clustering approach is widely used in high
dimensional data to improve the efficiency of K-means cluster solutions. Typically, Pearson …