Now showing items 1-3 of 3
k-means Performance Improvements with Centroid Calculation Heuristics both for Serial and Parallel environments
k-means is the most widely used clustering algorithm due to its fairly straightforward implementations in various problems. Meanwhile, when the number of clusters increase, the number of iterations also tend to slightly ...
Clustering Quality Improvement of k-means using a Hybrid Evolutionary Model
(ELSEVIER Science BV, 2015)
Choosing good candidates for the initial centroid selection process for compact clustering algorithms, such as k-means, is essential for clustering quality and performance. In this study, a novel hybrid evolutionary model ...
A Case Study for the Churn Prediction in Turksat Internet Service Subscription
(ASSOC Computing Machinery, 2015)
Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. ...