Abstract:

For the problems that the traditional method is greatly affected by the initialization center in the process of vehicle information data clustering， resulting in low clustering accuracy and poor robustness， and the selection of clustering center by calculating the mean in the iterative process is greatly affected by the outliers， a K-means complementary iterative vehicle information data clustering optimized by DHSSA is proposed. Firstly， for the problem of insufficient update of discoverer position and insufficient population diversity in SSA algorithm， a disturbance factor-head optimization strategy is designed. The influence of the optimal individual is strengthened by the adaptive head strategy， and the search space is expanded by the disturbance factor， which improves the accuracy of cluster center searching. Secondly， the initialization of cluster centers optimized by screening maximum and minimum distance product method （SMMP） is designed， and the screening mechanism is added on the basis of MMP， so that the initial centers are more evenly distributed in each cluster as much as possible. Finally， DHSSA and SMMP are integrated to optimize the K-means complementary iteration， which reduces the number of iterations and increases the search efficiency to obtain better clustering results. Using a variety of data sets for testing， through the convergence curve and performance indicators in the experimental results， it can be seen that the proposed DHSSA-KMC method is of higher search accuracy， convergence speed and lower clustering cost than SSA-KMC， IMFO-KMC， KMC and KMC++， and the time consumption is reduced compared with SSA-KMC and IMFO-KMC， which proves the effectiveness and superiority of the algorithm. In the process of vehicle information data processing， DHSSA-KMC can efficiently cluster and generate competitive models for consumers to choose， with obvious application value.

Key words: KMC, screening maximum and minimum distance product, SSA, data sets, car type information data