云模式事件混沌关联特征提取的 物联网大数据聚类算法.

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 validating 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, stability 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. [ABSTRACT FROM AUTHOR]