Phrase-based document similarity based on an index graph model

Document clustering techniques mostly rely on single term analysis of the document data set, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the document index graph, which indexes web documents based on phrases, rather than single terms only. The semi-structured web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The document index graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The similarity between documents is based on both single term weights and matching phrases weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, enhances web document clustering quality significantly.

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