云计算中基于群体智能算法的大数据聚类挖掘  

Big Data Clustering Mining Based on Swarm Intelligence Algorithm in Cloud Computing

作　　者：唐新宇 张新政[2] 赵月爱[3] TANG Xinyu;ZHANG Xinzheng;ZHAO Yueai(Department of Computer Application Technology, Guangdong College of Business and Technology, Zhaoqing 526040,China;School of Automation, Guangdong University of Technology, Guangzhou 510090, China;Department of Computer, Taiyuan Normal University, Jinzhong 030619, China)

机构地区：[1]广东工商职业学院计算机应用技术系,广东肇庆526040 [2]广东工业大学自动化学院,广州510090 [3]太原师范学院计算机系,山西晋中030619

出　　处：《重庆理工大学学报（自然科学）》2019年第4期128-133,167,共7页Journal of Chongqing University of Technology：Natural Science

基　　金：广东省教育厅高校特色创新类项目(自然科学)(2017GKTSCX110);广东省省级科技计划项目(2014A020217016)

摘　　要：为了提高数据挖掘的精度和效率,提出了一种基于群体智能算法的大数据聚类挖掘算法。首先对聚类算法中的模糊C-均值聚类算法进行分析,然后将亚启发式群体智能优化技术中的混合蛙跳算法与模糊C-均值聚类相结合,以便在调整的参数少的条件下优化全局搜索能力。仿真实验结果显示:相比其他聚类挖掘算法,提出的算法能解决局部陷阱问题,具有较好的聚类效果、准确率和收敛速度,同时算法的稳定性较高。In order to improve the accuracy and efficiency of data mining, a big data clustering mining algorithm based on swarm intelligence algorithm was proposed. Firstly, the fuzzy C -means clustering algorithm in clustering algorithm was analyzed. Then the hybrid leapfrog algorithm in the sub-heuristic group intelligent optimization technology was combined with the fuzzy C -means clustering to optimize the global search ability under the condition that the adjusted parameters were small. The simulation results show that compared with other clustering algorithms, the proposed algorithm can effectively solve the local trap problem, which has better clustering effect, accuracy and convergence speed. At the same time, the stability of the proposed algorithm is higher.

关 键 词：群体智能 大数据 聚类算法 混合蛙跳 模糊聚类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]