A Combinatorial Optimization Approach to Determining Optimal Data in Cluster

Abstract

Clustering is one of the data analysis activities for grouping data into several categories with the same characteristics based on certain criteria. The problem that often arises in the clustering process is getting optimal clustering results. So far there is no fixed provision to regulate the number of clusters and the type of data that must be placed in each cluster and also there is no optimal size for data grouping. By using a combinatorial optimization approach, a model that is able to group data optimally was developed. The solution was presented as a decision in the form of 0 and 1. The cluster data model was linearized to obtain cluster optimization. To obtain accurate information from a group of data, the results of this study can be used as an alternative solution for cluster optimization problems.