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A Fuzzy Based Evolutionary Multi-objective Clustering For Overlapping Clusters Detection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Malarkodi, Kumaravel Palanichamy Punithavathy, S. |
| Copyright Year | 2011 |
| Abstract | Abstract The term clustering refers to the identification of natural groups within a data set such that instances in the same group are more similar than instances in different groups. Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral shaped clusters. They suffer from the usual problem exhibited by evolutionary and unsupervised clustering approaches. In this thesis we proposed two different approaches to resolve the overlapping problems in complex shape data. The proposed method uses an evolutionary multi objective clustering approach with Genetic Algorithm (GA) using variable length chromosome and local search (memetic) and a Fuzzy based Multi objective Algorithm with variable length chromosome and local search (memetic). The Experimental results based on several artificial and real-world data show that the proposed Fuzzy based Evolutionary Multi objective Clustering for Overlapping Clusters (FEMCOC) can successfully identify overlapping clusters. It also succeeds obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the fuzzy based EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijser.org/researchpaper/A_Fuzzy_Based_Evolutionary_Multi-objective_Clustering_For_Overlapping_Clusters_Detection.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
