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Title: Incremental clustering of news reports
Authors: Azzopardi, Joel
Staff, Chris
Keywords: Document clustering
Cluster analysis
Semantic Web
News Web sites
Text data mining
Information retrieval
Issue Date: 2012
Publisher: MDPI
Citation: Azzopardi, J., & Staff, C. (2012). Incremental clustering of news reports. Algorithms, 5(3), 364-378.
Abstract: When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated processes may be required to help manage this information. In this paper, we describe a clustering system that can cluster news reports from disparate sources into event-centric clusters—i.e., clusters of news reports describing the same event. A user can identify any RSS feed as a source of news he/she would like to receive and our clustering system can cluster reports received from the separate RSS feeds as they arrive without knowing the number of clusters in advance. Our clustering system was designed to function well in an online incremental environment. In evaluating our system, we found that our system is very good in performing fine-grained clustering, but performs rather poorly when performing coarser-grained clustering.
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