A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

Syndrome types are important for diagnosis and treatment in traditional Chinese medicine. Syndrome types can be summarized by domain experts as formula clusters. In this paper, we propose seven feature models for the formula clustering problem based on categories, subcategories, functional tendencies and names of Chinese materia medica. A novel multi-objective clustering hyper-heuristic algorithm is obtained. In our proposed algorithm, 12 low-level heuristics are used for clustering solution perturbation by merging clusters, dividing clusters or moving points between clusters based on received solutions from the high-level heuristic. The high-level heuristic evaluates the received solutions from low-level heuristics, updates the solution pool, and selects initial solutions for the next iteration via roulette wheel selection on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms other clustering algorithms in most datasets. The initial number of clusters has less influence on the final clustering solutions for our proposed algorithm than for other clustering algorithms. For most datasets, the roulette wheel selection mechanism on the Pareto front shows higher convergence rates and accuracy than a random selection mechanism. Accuracy was higher for feature models based on functional tendencies than for the other feature models.