微型机与应用Issue(14)：78-81,4.
基于K-means的私人微博聚类算法改进
Improvements of personal weibo clustering algorithm based on K-means
高永兵 1郭文彦 1周环宇 1聂知秘1
作者信息
- 1. 内蒙古科技大学 信息工程学院，内蒙古 包头 014010
- 折叠
摘要
Abstract
Aiming at clustering research on personal weibo , an improved K-means algorithm is proposed on the combination of personal weibo content and structural features . By adding the reference and comment content into text , the influence of the server data sparseness in short documents is reduced . By screened out "micro topic" and improved the similarity computing , the appropri-ate categories and the number of initial centers is found , so the problems of K-means that the number of clusters K need to man-ually specify and the initial centers is random are solved . Experimental results show that the improved algorithm can not only get the adaptive value of K , but the accuracy is also improved compared with the general K-means .关键词
K-means 算法/私人微博/初始中心点/自适应Key words
K-means algorithm/personal weibo/initial centers/adaptive分类
计算机与自动化引用本文复制引用
高永兵,郭文彦,周环宇,聂知秘..基于K-means的私人微博聚类算法改进[J].微型机与应用,2014,(14):78-81,4.
