Name
Abstract | ||
|---|---|---|
Clustering is an unsupervised classification method where objects in the unlabeled data set are classified on the basis of some similarity measure. The conventional partitional clustering algorithms, e.g., K-Means, K-Medoids have several disadvantages such as the final solution is dependent on initial solution, they easily stuck into local optima. The nature inspired population based global search optimization methods offer to be more effective to overcome the deficiencies of the conventional partitional clustering methods as they possess several desired key features like up gradation of the candidate solutions iteratively, decentralization, parallel nature, and self organizing behavior. In this work, we compare the performance of widely applied evolutionary algorithms namely Genetic Algorithm (GA) and Differential Evolution (DE), and swarm intelligence methods namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) to find the clustering solutions by evaluating the quality of cluster with internal validity criteria, Sum of Square Error (SSE), which is based on compactness of cluster. Extensive results are compared based on three real and one synthetic data sets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIT.2014.67 | Information Technology |
Keywords | Field | DocType |
data handling,evolutionary computation,particle swarm optimisation,pattern clustering,search problems,unsupervised learning,ABC,DE,GA,PSO,SSE,artificial bee colony,differential evolution,evolutionary algorithms,evolutionary intelligence methods,genetic algorithm,global search optimization methods,particle swarm optimization,partitional clustering algorithms,partitional hard clustering,sum of square error,swarm intelligence methods,synthetic data sets,unlabeled data set,unsupervised classification method,Artificial Bee Colony,Data Clustering,Differential Evolution,Genetic Algorithm,Particle Swarm Optimization | Data mining,Canopy clustering algorithm,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Cluster analysis,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Prakash | 1 | 10 | 3.91 |
| Pramod Kumar Singh | 2 | 102 | 14.48 |