Cluster analysis by simulated annealing
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TL;DR
The results show that the algorithm which guaranteed obtaining a global optimum compared favourably with the traditional hierarchical technique and K-means algorithm.
Abstract
The present paper tries to apply a new clustering algorithm based on simulated annealing to chemometric research. A new stopping criterion and perturbation method which are more feasible than those proposed in the literature, are proposed. The algorithm is first tested on simulated data, and then used for the classification of Chinese tea samples. The results show that the algorithm which guaranteed obtaining a global optimum compared favourably with the traditional hierarchical technique and K-means algorithm.