Semantic indexing of hybrid frequent pattern-based clustering of documents with missing semantic information

Documents added recently to the web are augmented with semantic information to identify the class of the documents, i.e., the topic or concept to which document belongs to, can be identified explicitly using meta tags like keyword tags or rich data format RDF tags. But the documents that enriched the web five or ten years back do not contain semantic information. In this paper, we present hybrid clustering system using frequent pattern mining HCSFPM technique which fuses the two frequent pattern mining schemes: frequent term-based and frequent pattern-based techniques to cluster the documents according to topics or concepts. We also index the documents based on the semantic information content of the document. Results illustrate that HCSFPM method performs better than the traditional term-based method.