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PERFORMANCE ANALYSIS OF FUZZY CLUSTERING
| Content Provider | CiteSeerX |
|---|---|
| Author | Priyadharshini, S. P. Pujeri, Ra V. |
| Abstract | Abstract- Clustering algorithms is a process of break up the data objects into numerous groups which is similar. A K-means clustering algorithm is one of the most popular to used data objects into K cluster analysis. Fuzzy C-Means clustering algorithm is fashionable clustering method is more efficient, straightforward, easy to implement and sensitive to initialization. A demerit of K-Means and Fuzzy C-Means both algorithms are easily falling in local optima. Fuzzy Particle Swarm Optimization algorithm helps to solve local optima. Experimental results show that Fuzzy PSO approach giving highly competitive results for fuzzy clustering and FPSO outperform is better-quality the performance of other to be had algorithms. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Performance Analysis Fuzzy Clustering Data Object Local Optimum Fuzzy C-means K-means Clustering Algorithm Competitive Result Cluster Analysis Fuzzy Clustering Numerous Group Fuzzy Particle Swarm Optimization Algorithm Fpso Outperform Experimental Result Fuzzy Pso Approach Abstract Clustering Algorithm |
| Content Type | Text |
| Resource Type | Article |
