## 2 Background  
Document Clustering refers to the process whereby a collection of documents is clustered in an unsupervised manner into clusters such that documents within the same cluster are more similar to each other than to documents in other clusters [3,4]. Automatic Document Clustering is part of the larger domain of knowledge management and automatic document content analysis [3,5,5, 6, 7, 8]. It can serve as an intermediate step to perform other tasks [8]. The main justification behind the need for automatic document clustering systems is the information overload problem [4,6,7,9,10,11,12,13]. The amount of information available to users, especially textual data, has exploded with the advent of the World Wide Web and is thus becoming unlimited. There is a growing need for techniques to handle this increasing flood of incoming data and avoid delay in its distribution [6,9]. The presence of online sources of data continuously streaming out new documents, such as news sites and blogs, necessitates the use of incremental document clustering systems [13,14]. [PERSON] [10] distinguishes between off-line and online clustering algorithms whereby he claims that off-line algorithms are useful to organise static collections whilst incremental on-line algorithms are useful to organise dynamic corpora. In Topic Detection and Tracking (TDT) systems, the incoming stream/s of news reports are clustered in real-time as soon as each report is received [15]. The major issue encountered when performing Document Clustering is to perform effective clustering in an efficient manner [14]. [PERSON] [16] states that more complex linguistic models are being designed to have a better understanding of the syntax and semantics of natural languages in order to perform better clustering. However, such models may be too complex to be feasible to be applied to automatic document analysis. The issue of efficiency is felt more deeply in real time systems. According to [PERSON] [14], new event detection systems always assume that there are enough resources available to perform the necessary computation, and thus they only focus on accuracy and do not give efficiency its proper attention. [PERSON] [17] also highlights the importance of efficient clustering especially when operating on large domains like regular news feeds. The Document Clustering process typically consists of [7]: the extraction of features from the documents; the mapping of the documents to high-dimensional space; and the clustering of the points within the high-dimensional space. Document features are usually represented by the set or a subset of the words they contain [4,6,7,9,10,11,12,13,14,16,17,18,19,20,21,22]. This approach is also known as the Bag-of-Words approach. [PERSON] [6] advocates the use of pre-selected terms to represent each document. In contrast, [7,9,14,16,18,20,23] extract all the terms from the documents to act as content-representatives--albeit using some filtering sometimes, such as using only the highest-weighted \(n\) terms [7]. In the majority of cases, stop word lists are used to remove words that occur too frequently (e.g., \"the\", \"of\", \"and\",...), and suffix stripping routines (such as [PERSON]'s stemming routine [24]) are used to reduce words to their stems [7,14,16,18,19,20,22,23,25]. The importance of a term as a representative of the document's content may be calculated using the Inverse Document Frequency (IDF), which is the ratio of the term occurrence frequency within the document in question to the occurrence frequency of the term over the whole document collection [18]. [PERSON] [12] calculates the term weights by utilising the TF.IDF measure that is centred on the DMOZ (Open Directory Project) Categories. The advantage of this approach is that one does not need to have the entire document collection at hand to weight the documents' index terms.