Now showing items 1-4 of 4
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 ...
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 ...
Generic Menu Optimization for Multi-profile Customer Systems
The use of optimal ATM menu structuring for different customer profiles is essential because of usability, efficiency, and customer satisfaction. Especially in competitive industries such as banking, having optimal user ...
High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm
Achieving high quality clustering is one of the most well-known problems in data mining. k-means is by far the most commonly used clustering algorithm. It converges fairly quickly, but achieving a good solution is not ...