Abstract | ||
---|---|---|
Clustering is an unsupervised learning method that is used to group similar objects. One of the most popular and efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method that hybridizes Invasive Weed Optimization (IWO) and K-means. The IWO algorithm is a recent population based method to iteratively improve the given population of a solution. In this study, the algorithm is used in the initial stage to generate a good quality solution for the second stage. The solutions generated by the IWO algorithm are used as initial solutions for the K-means algorithm. The proposed hybrid method is evaluated over several real world instances and the results are compared with well-known clustering methods in the literature. Results show that the proposed method is promising compared to other methods. |
Year | Venue | Keywords |
---|---|---|
2015 | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY | Data clustering, K-means algorithm, IWO, hybrid evolutionary optimization algorithm, unsupervised learning |
Field | DocType | Volume |
k-means clustering,Canopy clustering algorithm,Population,CURE data clustering algorithm,Pattern recognition,Computer science,Local optimum,Artificial intelligence,Time complexity,Cluster analysis,Population-based incremental learning,Machine learning | Journal | 12 |
Issue | ISSN | Citations |
5 | 1683-3198 | 1 |
PageRank | References | Authors |
0.35 | 12 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Fatemeh Boobord | 1 | 1 | 0.35 |
Zalinda Othman | 2 | 146 | 7.63 |
Azuraliza Abu Bakar | 3 | 157 | 30.29 |