Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
Communications of the ACM - The Blogosphere
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 17th international conference on World Wide Web
Analyzing and visualizing gray web forum structure
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
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Due to the advance of Web 2.0 technologies, a large volume of web opinions are available in computer-mediated communication sites such as forums and blogs. Many of these web opinions involve terrorism and crime related issues. For instances, some terrorist groups may use web forums to propagandize their ideology, some may post threaten messages, and some criminals may recruit members or identify victims through web social networks. Analyzing and clustering Web opinions are extremely challenging. Unlike regular documents, web opinions usually appear as short and sparse text messages. Using typical document clustering techniques on web opinions produce unsatisfying result. In this work, we propose the scalable distance-based clustering technique for web opinions clustering. We have conducted experiments and benchmarked with the density-based algorithm. It shows that it obtains higher micro and macro accuracy. This web opinions clustering technique is useful in identifying the themes of discussions in web social networks and studying their development as well as the interactions of active participants.