## 1 Introduction  
Within some time of an event happening in the real world, numerous news reports will appear on news sites on the World Wide Web that describe that event. Moreover, as time passes, new reports will appear that give information on how that event developed and what its effects were. Huge quantities of information presented to user may lead to confusion on the side of the user, and some knowledge contained within this information may remain hidden since the user may not have time to go through the entire corpus of information to get each nugget of information, or may miss it as it lies \"buried\" in familiar information.  
Automated processes such as Document Fusion and Recommendation systems can be used to assist the user in his/her quest for knowledge discovery. The tasks of such automated systems may be rendered simpler by having a mechanism that clusters news stories together by specific events (e.g., news reports on the murders in Norway by [PERSON]). Although existing news aggregators (e.g., Google News) provide this functionality, the user would be limited to the news stories that the aggregator tracks. We anticipate that users may wish to choose their own news-feeds so we have implemented a modified version of a clustering algorithm.  
In this research, we have implemented a clustering system that uses a modified k-means clustering algorithm adapted for incremental clustering to cluster news reports into event-centric clusters. News reports are downloaded continuously through RSS, and these are clustered \"on the fly\", resulting in the creation of new clusters or the updating of previously existing clusters. The number of clusters is not known in advance, and new events are detected automatically. Since the stream of news reports never terminates, cluster reorganisation is not feasible unlike the \"standard\" k-means algorithm.  
Evaluation results show that the algorithm used to cluster news reports works very well when the clustering required is highly specific--_i.e._, when each output cluster should contain only news reports describing a specific event (e.g., news reports describing a particular murder in Malta are clustered separately from news reports describing a murder in Italy). On the other hand, in cases where the requested output clusters are more generic--e.g., clustering all news reports about sports, our system obtains worse results than the baseline system (the standard k-means system). However, since our aim in this research is the clustering of reports into event clusters, our document clustering system is suitable for our purposes.  
Our document clustering system was implemented to be the initial part of a larger system that performs news report fusion and issues personalised recommendations--refer to [1,2] for more details. Our system downloads RSS feeds from 9 different news sources (including Maltese local sources and international sources such as Reuters). The categorisation process of a single news report, on average, takes less than 1 second on a Linux quad-core system with CPUs running at 2.4 GHz and 4 MB of cache, and with 8 GB of RAM.  
This paper proceeds as follows: In Section 2 we give an overview of related systems in literature; we describe our clustering system in Section 3; Section 4 describes how we performed our evaluation and presents the results obtained; and finally we present our conclusions in Section 5.