CLUSTER-ANALYSIS BY SIMULATED ANNEALING

The present paper tries to apply a new clustering algorithm based on s imulated annealing to chemometric research. A new stopping criterion a nd perturbation method which are more feasible than those proposed in the literature, are proposed. The algorithm is first tested on simulat ed data, and then used for the classification of Chinese tea samples. The results show that the algorithm which guaranteed obtaining a globa l optimum compared favourably with the traditional hierarchical techni que and K-means algorithm.