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. 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. Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. As we describe in (Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 3 July, 2015. Bogazici University Printhouse. http: www.issi2015.org files downloads all-papers 1042.pdf, 2015), the metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. However, this semantic matrix does not allow to calculate similarities between articles directly. In this paper, we will describe in detail how we build a semantic representation for an article from the entities that are associated with it. Base on such semantic representations of articles, we apply two standard clustering methods, K-Means and the Louvain community detection algorithm, which leads to our two clustering solutions labelled as OCLC-31 (standing for K-Means) and OCLC-Louvain (standing for Louvain). In this paper, we will give the implementation details and a basic comparison with other clustering solutions that are reported in this special issue. The increasing diffusion of XML languages for the encoding of domain-specific multimedia information raises the need for new information retrieval models that can fully exploit structural information. An XML language specifically designed for music like MX allows queries to be made directly on the thematic material. The main advantage of such a system is that it can handle symbolic, notational, and audio objects at the same time through a multilayered structure. On the model side, common music information retrieval methods do not take into account the inner structure of melodic themes and the metric relationships between notes. In this article we deal with two main topics: a novel architecture based on a new XML language for music and a new model of melodic themes based on graph theory. This model takes advantage of particular graph invariants that can be linked to melodic themes as metadata in order to characterize all their possible modifications through specific transformations and that can be exploited in filtering algorithms. We provide a similarity function and show through an evaluation stage how it improves existing methods, particularly in the case of same-structured themes.