Now showing items 1-3 of 3
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 ...
Generalized Class Representative Computation with Graph Embedding and Clustering
In this paper, we propose an object category representation framework by first showing objects as graph structures and embedding graphs into vector spaces for color object recognition. The object categories are then built ...