| Abstrakt: |
Clustering is a popular topic in data analysis and pattern recognition research. This paper presents an efficient memetic algorithm for clustering tasks. It applies to the whole simulated annealing process rather than those popular methods, only using metropolis criterion, as the local search mechanism to refine the differential evolutionary for improving the accuracy and robustness. The results show that this algorithm performs better than several existing methods in terms of clustering accuracy and efficiency in the majority of the three synthetic and four real life data sets used in this study. Moreover, the presented algorithm is more robust, flexible and not sensitive to the initial value in the unbalanced, overlapped and noisy data sets. [ABSTRACT FROM AUTHOR] |
