A multi-level framework for the analysis of sequential data

Authors:
Carl H. Mooney;Denise de Vries;John F. Roddick
Affiliations:
School of Informatics and Engineering, Flinders University of South Australia, Adelaide, South Australia;School of Informatics and Engineering, Flinders University of South Australia, Adelaide, South Australia;School of Informatics and Engineering, Flinders University of South Australia, Adelaide, South Australia
Venue:
Data Mining
Year:
2006

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Abstract

Traditionally text mining has had a strong link with information retrieval and classification and has largely aimed to classify documents according to embedded knowledge. Association rule mining and sequence mining, on the other hand, have had a different goal; one of eliciting relationships within or about the data being mined. Recently there has been research conducted using sequence mining techniques on digital document collections by treating the text as sequential data. In this paper we propose a multi-level framework that is applicable to text analysis and that improves the knowledge discovery process by finding additional or hitherto unknown relationships within the data being mined. We believe that this can lead to the detection or fine tuning of the context of documents under consideration and may lead to a more informed classification of those documents. Moreover, since we use a semantic map at varying stages in the framework, we are able to impose a greater degree of focus and therefore a greater transitivity of semantic relatedness that facilitates the improvement in the knowledge discovery process.