Quantum genetics clustering algorithm based on high-Dimensional and multi-chain coding scheme

Lin Bie, Rui Zhang, Zhiteng Wang

Abstract: The initial clustering center of the traditional K-means algorithm is randomly generated from the data set which leads it easily gets the local optimal solution rather than the global optimal solution. In this paper, a high-dimensional quantum genetic clustering method is proposed in which each quantum bit is decomposed into a plurality of parallel genes according to a high-dimensional quantum encoding scheme and it effectively expands the search space and enhances the efficiency of parallel search. A quantum updating strategy comes out by combining the high-dimensional coding scheme and the dynamically adjustment rotation angle mechanism to renew the individual. Quantum mutation is implemented by quantum non-gate mutation strategy to enhance the global searching ability of the algorithm. Experiments show that the algorithm is not only better in clustering accuracy, but also faster in convergence.