An Iterative Clustering Method for the XML-Mining Task of the INEX 2010

Tovar, Mireya; Cruz, Adrián; Vázquez, Blanca; Pinto, David; Vilariño, Darnes; Montes, Azucena

doi:10.1007/978-3-642-23577-1_36

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…In Tovar et al [29] the authors propose two iterative clustering methods for grouping Wikipedia documents into clusters. They use a recursive clustering process iteratively on a subset of the complete set.…”

Section: Related Workmentioning

confidence: 99%

Towards improving XML search by using structure clustering technique

Shalabi

Elfatatry

2014

Journal of Information Science

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Searching large XML repositories is a challenging research problem. The application of clustering on a large repository before performing a search enhances the search process significantly. Clustering reduces a search space into smaller XML collections that can be better searched. In this work, we present an enhanced XML clustering by structure method. Also, we introduce a new representation of XML structure that keeps all characteristics of XML structure without summarization. Then, we perform a benchmark comparison between the search results of our improved method to SAXON and Qizx XML XQuery processors. The comparison focuses on search processing time and accuracy of the results using different sizes of datasets for both homogeneous and heterogeneous XML documents. The attained results show better accuracy at the same level of performance.

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Section: Related Workmentioning

confidence: 99%

Towards improving XML search by using structure clustering technique

Shalabi

Elfatatry

2014

Journal of Information Science

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“…A novel approach to document clustering evaluation was introduced at INEX in 2009 [26] and 2010 [8]. It used ad hoc information retrieval to evaluate document clustering by using relevance judgments from retrieval systems in the ad hoc track [34]. Ad hoc information retrieval evaluation is a system based approach that evaluates how different systems rank relevant documents.…”

Section: Inex Xml Mining Trackmentioning

confidence: 99%

“…The Structured Linked Vector Model (SLVM) [37] incorporates document structure, links and content. The k-star [34] is an iterative clustering method for grouping documents. The TopSig approach [16] produces binary strings that represent documents and a modified k-means algorithm that works directly with this representation.…”

Section: Divergence From a Random Baselinementioning

confidence: 99%

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Document Clustering Evaluation: Divergence from a Random Baseline

De Vries,

Geva,

Trotman

2012

Preprint

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Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality. This includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval. The divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the INEX XML Mining track. It also appears to perform a normalisation similar to the Normalised Mutual Information (NMI) measure but it can be applied to any measure of cluster quality. When it is applied to the intrinsic measure of distortion as measured by RMSE, subtraction from a random baseline provides a clear optimum that is not apparent otherwise. This approach can be applied to any clustering evaluation. This paper describes its use in the context of document clustering evaluation.

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“…The group from BUAP [10] proposed an iterative clustering method for grouping the Wikipedia documents. The recursive clustering process iteratively brings together subsets of the complete collection by using two different clustering methods: k-star and k-means.…”

Section: Clustering Participants Submissions and Evaluationsmentioning

confidence: 99%