基于参与者共现分析的博文聚类研究  

Clustering Blog Posts with Co-occurrence Analysis

作　　者：龚凯乐 成颖[1] 孙建军[1] 

机构地区：[1]南京大学信息管理学院,南京210023

出　　处：《现代图书情报技术》2016年第10期50-58,共9页New Technology of Library and Information Service

基　　金：国家自然科学基金面上项目"融合范式视角下的链接分析理论集成框架及其实证研究"(项目编号:71273125);中国科学技术信息研究所合作研究项目的研究成果之一

摘　　要：【目的】将博文参与者共现作为特征,探析其在博文聚类中的价值。【方法】两步聚类:构建不同博文参与者的共现矩阵并转化为相关矩阵,采用近邻传播(Affinity Propagation,AP)算法完成第一步聚类;将AP聚类结果的质心作为初始聚类中心,对词项进行位置加权,利用K-means算法完成博文内容的第二步聚类。【结果】综合博文参与者共现与词项位置加权的聚类算法平均准确率与纯度分别达到0.66和0.57,显著优于对比实验。【局限】本研究的主要贡献是引入参与者共现作为特征改进博文聚类效果,对于该特征甚少的博文聚类价值有限。【结论】整合词项与博文参与者特征的博文聚类显著地提高了聚类质量,两步法聚类也为K-means算法初始聚类中心的选择提供了可行的解决方案。[Objective] This study investigates the co-occurrence of blog comment contributors, aiming to explore their roles in blog posts clustering. [Methods] We developed a method of two-step clustering. First, we constructed the co-occurrence matrix of the contributors from different blog posts and then transform it to a correlation matrix. Then finished the first-step clustering with the help of Affinity Propagation （AP） algorithm. Second, we calculated the terms＇ position weight based on the centers of AP clustering, and then finished the second-stage blog post content clustering with K-means algorithm. [Results] The average precision and recall ratio of the proposed method were 0.66 and 0.57, which were significantly higher than those of the traditional ones. [Limitations] The blog comment contributors co-occurrence improved the quality of clustering, but it has limited value in blog posts with few comments. [Conclusions] The proposed method improves the quality ofblog posts clustering by combining terms and contributors＇ co-occurrence. The two-step clustering method is a better option to select the initial cluster centers of the K-means algorithm.

关 键 词：共现分析 文本聚类 博文参与者 初始聚类中心 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]