Table 2 - uploaded by Ahmed Elfatatry
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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 met...
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... n r i is the number of documents in the generated cluster C i belonging to actual modelled category, n r is the number of XML documents belong to the modelled category, and n i is the number of XML documents categorized by the clus- tering algorithm. The result of the accuracy comparison between EXCLS, XCLS and XEdge for the first experiment is shown in Table 2. The input parameter for XCLS algorithm is threshold value 0.9 and the input parameter for XEdge is the number of clusters (k), which equals 4. It is evident that XCLS failed to assign the documents to their clusters cor- rectly, while EXCLS and XEdge did a perfect job and clustered the documents correctly. ...
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Citations
... Create a mediated schema for integrating approach for XML structures has discussed by Saleem et al. [15], they used linguistic matchers that extract semantics of all node labels and tree-mining data structure and label clusters to find node context. Structure method for enhancing XML clustering without summarize characteristics of XML structure is used by Shalabi and Elfatatry [16], the technique treats with different sizes of homogeneous and heterogeneous XML documents datasets. Al Hamad [17] developed a mediate schema for integrating heterogeneous XML, the technique decomposes the original schema into subschemas using three levels ancestor, root, and leaf. ...
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... The authors in [27] clustered XML documents via PathXP algorithms. PathXP allows to groups documents according to their characteristic features rather than their direct similarity. ...
The main objective of the work is to improve the clustering efficiency and performance when we deal with very big datasets. This paper aims to improve the quality of XML data clustering by exploiting more features extracted from source schemas. In particular, it proposes clustering approach that gathers both content and structure of XML documents to determine similarity between them. The content and structure information are concluded using two different similarity methods that are then grouped via weight factor to compute the overall document similarity. The structural similarity of XML data are derived from edge summaries while content features similarity are derived from aggregate of set of similarity measures; Jaccard, Cosine measure and Jensen-Shannon divergence in one algorithm. However, we also experimented using Jaccard distance as content measure with edge summaries to prove that using an aggregation of content similarity measures can further improve the results. The experiments prove that clustering of XML documents based on structure only information produce worse solution in homogenous environment, while in heterogeneous environment clustering of XML document produce better result when the structure and the content are combined. Results have shown that performance and quality of the proposed approach is better in comparison of both XEdge and XCLSC approaches.
