## 3 Methodology  
The main aim within this research is the development of a Document Clustering system that is online and incremental--the system needs to continuously receive news reports, cluster them \"on the fly\" resulting in the creation of new clusters or the updating of previously existing clusters, and pass these clusters to the other systems as necessary for further processing. Therefore, this system cannot employ complex linguistic models or use multi-pass clustering approaches, as these will result in higher processing times that are not feasible in our operational environment. Also, cluster re-organisation is not possible, since it will result in an interruption of the operational system. The required output is a single level of event-centric clusters--there is no need to have hierarchies of news reports in our system. The main steps performed by our clustering system are shown in Figure 1. Our clustering system obtains its source news reports via the RSS feeds. The RSS feeds from the selected sources are downloaded periodically and parsed. The relevant news reports are then downloaded, if they had not already been downloaded previously, and cleaned from the surrounding text and HTML code in such a way that only the news report content remains as simple text. The incoming news reports are then represented in high-dimensional space using the Bag-of-Words model, following the approach taken by the majority of systems reviewed in Section 2. The set of words in each of the news reports are filtered using stop-words lists, and [PERSON]'s suffix stemming routine [24] is applied to reduce words to their stems. The reasons for the selection of the Bag-of-Words approach is that it is simple and efficient and does not require any supervision or any other intervention. Moreover, in our opinion, its wide-spread use represents sufficient proof of its effectiveness. The index terms are weighted using the TF.IDF measure. Since the collection of news reports is incremental, the document frequency of each term refers to the number of times that term has appeared in the news report collection until the time that the processing is being done. This is similar to the approach followed in [19]. Our system maintains a global index that stores the occurrence of each stemmed term within the entire news report collection. This global index is updated incrementally as the incoming news reports are being processed. A separate index is also created for each news report being processed storing the occurrence frequencies of the different terms in the corresponding news report. Term weights are not stored but are calculated dynamically when needed. For the case where the system has just started processing its first documents, a special procedure is performed for the weighting of those documents' terms. Before starting the categorisation process, the system waits for the first 70 documents to be available for processing, and initialises a global index with the occurrence frequencies of these documents' terms within this initial collection of 70 documents. We monitor 9 RSS feeds (4 from international news sites such as Reuters Top News, and 5 from Maltese news sites), and it takes approximately 12 h to collect 70 breaking news reports from these feeds. In our opinion, an index built from 70 news reports provides ample indication of which terms are important, and which terms are common throughout the entire collection. The clustering of these 70 documents proceeds normally with the sole exception that the global index is not re-updated whilst the clustering of these 70 documents is being performed. When processing the first document, there are no existing categories, and so it is considered to be a \"new\" event. Once the news reports have been converted to their corresponding representation, the similarities between the different news reports are calculated as necessary. Each similarity is measured using the distance function that is most widely used within the reviewed document clustering systems--the Cosine Similarity measure.