DHSSA优化的K均值互补迭代车型信息数据聚类

K-means Complementary Iterative Vehicle Information Data Clustering Based on DHSSA Optimization

针对传统方法在车型信息数据聚类过程中受初始化中心点的影响较大导致聚类精度低、鲁棒性差以及在迭代过程中求取均值选择聚类中心受离群点影响大的问题,提出了一种DHSSA优化的K均值互补迭代车型信息数据聚类方法。首先,针对SSA算法中发现者位置更新不足和种群多样性不足的问题,设计了一种扰动因子-领头雀优化策略,通过自适应领头雀策略加强了最优个体的影响力,利用扰动因子扩大搜索空间,提升了寻找聚类中心的准确率;其次,设计了基于筛选最大最小距离积方法 SMMP优化聚类中心的初始化,在MMP基础上增加了筛选机制,使初始化的中心尽可能更均匀地分布在每个簇中;最后,融合DHSSA和SMMP来优化K均值互补迭代,在减小迭代次数的同时增加搜索效率,得到较好的聚类结果。利用多种数据集进行测试,通过试验结果中的收敛曲线和性能指标可以看出,提出的DHSSA-KMC方法相对于SSA-KMC、IMFO-KMC、KMC和KMC++具有更高的搜索精度、收敛速度和更低的聚类代价,并且耗时相对于SSA-KMC和IMFO-KMC有所减少,证明了算法的有效性和优越性。在车型信息数据处理过程中,DHSSA-KMC可以高效聚类生成竞品车型供消费者选择,应用价值明显。

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 Kmeans 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 DHSSAKMC method is of higher search accuracy,convergence speed and lower clustering cost than SSA-KMC,IMFOKMC,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.