This paper presents some experiments in clustering homogeneous XML documents to validate an existing classification or more generally an organisational structure. Our approach integrates techniques for extracting knowledge from docu- ments with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging clas- sification. We mix the selection of structured features with fine textual selection based on syntactic characteristics. We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria.
XML document clustering has been used mostly for visualizing large collections of documents, for example @cite_2 cluster AML (Astronomical Markup Language) documents based only on their links. @cite_3 propose a model similar to @cite_1 but adding in- and out-links to the model, and they use it for clustering rather than classification. @cite_4 also propose a BitCube model for clustering that represents documents based on their ePaths (paths of text elements) and textual content. Their focus is on evaluating time performance rather than clustering effectiveness.
@cite_4 @cite_1 @cite_3 @cite_2
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In this paper, we describe a new bitmap indexing technique to cluster XML documents. XML is a new standard for exchanging and representing information on the Internet. Documents can be hierarchically represented by XML-elements. XML documents are represented and indexed using a bitmap indexing technique. We define the similarity and popularity operations available in bitmap indexes and propose a method for partitioning a XML document set. Furthermore, a 2-dimensional bitmap index is extended to a 3dimensional bitmap index, called BitCube. We define statistical measurements in the BitCube: mean, mode, standard derivation, and correlation coefficient. Based on these measurements, we also define the slice, project, and dice operations on a BitCube. BitCube can be manipulated efficiently and improves the performance of document retrieval. In this paper, w edescribe a novel text classi er that can e ectiv ely cope with structured documents. We report experiments that compare its performance with that of a wellknown probabilistic classi er. Our novel classi er can take adv antage of the information in the structure of document that con ventional, purely term-based classi ers ignore.Conventional classi ers are mostly based on the vector space model of document, which views a document simply as an n-dimensional vector of terms. T o retain the information in the structure, w e ha ve dev eloped a structured vector model, which represents a document with a structured vector, whose elements can be either terms or other structured vectors. With this extended model, we also have improved the well-kno wn probabilistic classi cation method based on the Bernoulli document generation model. Our classi er based on these improvements performes signi cantly better on pre-classi ed samples from the web and the US Patent database than the usual classi ers. A semi-structured document has more structured information compared to an ordinary document, and the relation among semi-structured documents can be fully utilized. In order to take advantage of the structure and link information in a semi-structured document for better mining, a structured link vector model (SLVM) is presented in this paper, where a vector represents a document, and vectors' elements are determined by terms, document structure and neighboring documents. Text mining based on SLVM is described in the procedure of K-means for briefness and clarity: calculating document similarity and calculating cluster center. The clustering based on SLVM performs significantly better than that based on a conventional vector space model in the experiments, and its F value increases from 0.65-0.73 to 0.82-0.86. Abstract Self-organization or clustering of data objects can be a powerful aid towards knowledge discovery in distributed databases. The web presents opportunities for such clustering of documents and other data objects. This potential will be even more pronounced when XML becomes widely used over the next few years. Based on clustering of XML links, we explore a visualization approach for discovering knowledge on the web.