Title :
Cluster Based Detection and Analysis of Internet Topics
Author :
Wu, Jiao ; Gao, Weihua ; Zhang, Bin ; Liu, Jinsong ; Li, Chao
Author_Institution :
News Center, Hebei Univ., Baoding, China
Abstract :
Internet topic detection and classification is an intelligent information access technology. It studies how to detect new events and classify sentiment of the content. Classical detection and analysis system of internet topics has low analysis efficiency and large process delay. The functions of cluster-based analysis system are internet data collection, real-time analysis and off-line data analysis. Experimental results show that the Average Job Time (AJT) and Average Waiting Time (AWT) for jobs in case of Service Cluster are comparatively lesser with respect to Physical Server, and the Service Cluster shortens the service failover time by 93.4%.
Keywords :
Internet; pattern classification; pattern clustering; Internet topic clasification; Internet topic detection; average job time; average waiting time; cluster based detection; intelligent information access technology; Conferences; Data analysis; Data warehouses; Engines; Internet; Servers; Web pages; Cluster; Job Scheduling; data analysis; internet topic;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4577-1085-8
DOI :
10.1109/ISCID.2011.195
