云模式事件混沌关联特征提取的物联网大数据聚类算法  

Cloud pattern clustering algorithms on big data of Internet of Things based on extracting chaotic correlation features of events

作　　者：王雪蓉[1] 万年红[2] Wang Xuerong;Wan Nianhong(Dept.of Teaching Work,Zhejiang Dongfang Polytechnic,Wenzhou Zhejiang 325000,China;School of Digital Engineering,Zhejiang Dongfang Polytechnic,Wenzhou Zhejiang 325000,China)

机构地区：[1]浙江东方职业技术学院教学工作部,浙江温州325000 [2]浙江东方职业技术学院数字工程学院,浙江温州325000

出　　处：《计算机应用研究》2021年第2期391-397,共7页Application Research of Computers

摘　　要：目前的聚类方法单纯从某个角度研究数据聚类问题,对基于云模式的混沌的物联网大数据聚类的考虑不足,聚类质量不高。为实现敏捷、智能、平稳的物联网大数据聚类,基于开展物联网事件的云模式通用描述模型、物联网事件混沌关联特征的云模式通用解析模型、基于云模式的物联网事件混沌关联特征提取算法、基于云模式混沌关联特征的物联网大数据关联挖掘研究,改进分解奇异值算法、网格耦合聚类算法、K-means算法、决策树学习法、分析主成分法、分层合并法等算法和分布概率函数,设计了一种基于事件混沌关联特征、敏捷、智能、平稳的物联网大数据聚类算法。最后,开展实验验证,并与传统算法进行性能对比分析。实验结果表明,相比传统算法,该算法聚类时间短、误差小,且敏捷性、智能性、动态演化性和平稳性高。因此,该算法实现了基于云模式的具有混沌关联特征的物联网事件大数据的有效聚类,具有较高的应用价值。Current clustering methods study data clustering problems only from one angle,it is insufficient to considerate clustering chaotic big data of Internet of Things based on cloud pattern with low clustering quality.To achieve agile,intelligent and stable clustering on big data of Internet of Things,with studying general cloud pattern description models on events of Internet of Things,general cloud pattern analysis models on chaotic correlation features of events of Internet of Things,extracting algorithms on chaotic correlation features of events of Internet of Things based on cloud pattern,correlation mining of big data of Internet of Things based on cloud pattern chaotic correlation features,improved decompositing singular value algorithms,grid coupling clustering algorithms,K-means algorithms,decision tree learning methods,methods of analysis principal components,stratification merging methods and distribution probability function,this paper designed an agile,intelligent and stable clustering algorithm on big data of Internet of Things based on chaotic correlation features of events.Finally,it carried out vali-dating experiments,and compared performance of this proposed algorithm with traditional algorithms.Experimental results show this algorithm has shorter clustering time,less error and higher agility,has better intelligence,dynamic evolution,stabi-lity than those of traditional algorithms.Therefore,this proposed algorithm achieves effective clustering on big data of events of Internet of Things with chaotic correlation features based on cloud patterns,has higher utility.

关 键 词：物联网事件 云模式 混沌关联特征 关联挖掘 大数据聚类算法 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]