Title :
An Improved Agglomerative Levels K-Means Clustering Algorithm
Author :
Yu Jiankun ; Guo Jun
Author_Institution :
Sch. of Inf., Yunnan Univ. of Finance & Econ., Kunming, China
Abstract :
The paper proposed a method which combines an improved hierarchical aggregation and K-means clustering algorithm, overcoming the selection problem of initial cluster centers and selection problem of termination condition. Application this method to cluster sina weibo topic and compare with tradition hierarchical aggregation and K-means clustering algorithm, finding the method can reduce false positives and missed rate.
Keywords :
Web sites; pattern clustering; Sina Weibo topic clustering; agglomerative level k-means clustering algorithm; hierarchical aggregation; initial cluster center selection problem; termination condition selection problem; Algorithm design and analysis; Approximation algorithms; Clustering algorithms; Data mining; Educational institutions; Feature extraction; Time complexity; Agglomerative hierarchical clustering; K-means; initial cancroids; termination condition;
Conference_Titel :
Management of e-Commerce and e-Government (ICMeCG), 2014 International Conference on
DOI :
10.1109/ICMeCG.2014.53
