基于图数据模型的聚类方法及可信度检测  

Clustering Method Based on Graph Data Model and Reliability Detection

作　　者：程艳云[1] 边荟凇 边长生 Cheng Yanyun;Bian Huisong;Bran Changsheng(Nanjing University Of Posts And Telecommunications, Nanjing 210023, China;Nanjing College of Information Technology, Nanjing 210023, China)

机构地区：[1]南京邮电大学自动化学院,江苏南京210023 [2]南京信息职业技术学院,江苏南京210023

出　　处：《系统仿真学报》2018年第6期2102-2108,2116,共8页Journal of System Simulation

基　　金：江苏省省级现代服务业(软件产业)发展专项引导资金(SJ214038)

摘　　要：对于特征空间中的数据,传统聚类算法通常直接在特征空间中进行聚类分析,因此高维空间数据无法在二维平面实现直观有效的聚类结果图形可视化,图数据可以明确反映对象之间的相似性关系,根据数据对象之间的距离,通过迭代将特征空间的数据建模成图数据。并对建模得到的图数据模型进行基于模块性的聚类分析,实现对非凸球分布数据集的聚类及对聚类结果实现二维空间的图形可视化。提出了聚类结果关于类间邻近边界的可信度概念,并提出了一种利用PageRank算法实现对聚类结果可信度计算的方法。For the data in feature space, traditional clustering algorithm can take clustering analysis directly. High-dimensional spatial data cannot achieve intuitive and effective graphical visualization of clustering results in 2D plane. Graph data can clearly reflect the similarity relationship between objects. According to the distance of the data objects, the feature space data are modeled as graph data by iteration. Cluster analysis based on modularity is carried out on the modeling graph data. The two-dimensional visualization of non-spherical-shape distribution data cluster and result is achieved. The concept of credibility of the clustering result is proposed, and a method is proposed, which the Page Rank algorithm is used to calculate the reliability of clustering results.

关 键 词：数据挖掘 聚类 图数据建模 模块性 PAGERANK算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]