Improve K-Means Algorithm

The algorithm classifies objects to a predefined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each other. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This research focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This research uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid.

What-If Analysis Simulation Model in Business Intelligence Application on Offers’ Selection in Syriatel Telecom Company

Decision making process have to be much more accurate and careful. Therefore, decision makers depend on what-if analysis systems to predict an impact of a specific scenario. Usually, previous what-if analysis models in the literature have been directed just to predict an impact of a specific scenario. Therefore, our main goal in this approach is to enhance what-if analysis to suggest the best scenarios, in addition to predict their impacts. Affordable offers are one of the best ways to increase the revenue in telecom companies. Decision makers can predict a potential revenue before launching an offer, depending on what-if analysis system. This research depends on enhanced k-means algorithm to categorize customers into segments of the same behavior or usage.