现代电子技术2017，Vol.40Issue(19)：177-181,5.DOI:10.16652/j.issn.1004-373x.2017.19.047
优先聚类和高斯混合模型树相融合的递增聚类研究
Research on incremental clustering integrating priority clustering with Gaussian mixture model tree
资和周1
作者信息
- 1. 云南经济管理学院 财经商贸学院,云南 昆明 650106
- 折叠
摘要
Abstract
The traditional clustering algorithms consume a large amount of time and memory for large dataset clustering, can′t adapt to the dynamic performance of big data flow,and have poor clustering stability. Therefore,an incremental clustering method based on partial-priority clustering and Gaussian mixture model tree is put forward. The partial-priority clustering algo-rithm is used to perform the priority clustering for large dataset,acquire the typical dataset,and reduce the data complexity of large dataset. And then the incremental clustering algorithm based on Gaussian mixture model tree is used to insert the data in typical dataset into a Gaussian mixture model tree to construct the Gaussian mixture model tree of the dataset. The leaf nodes and none-leaf nodes of the tree are matched with single Gaussian data distribution and Gaussian mixture model distribution re-spectively. According the insertion results,the Gaussian mixture model tree is adjusted,the data inserted into the model should be deleted whether or not is detected,and data deletion is accomplished. The breadth-first method is adopted to get the best tree node as the final clustering result. The experimental results indicate that the proposed incremental clustering algorithm has per-fect clustering effect,strong expansibility,and high stability.关键词
大数据/聚类分析/高斯混合模型/仿真实验Key words
big data/clustering analysis/Gaussian mixture model/simulation experiment分类
信息技术与安全科学引用本文复制引用
资和周..优先聚类和高斯混合模型树相融合的递增聚类研究[J].现代电子技术,2017,40(19):177-181,5.
