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 ...
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 ...