Data Mining pp 229-243 | Cite as

A Multi-level Framework for the Analysis of Sequential Data

  • Carl H. Mooney
  • Denise de Vries
  • John F. Roddick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3755)

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.

Keywords

Semantic Similarity Salience Function Edit Distance Association Rule Mining Support Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carl H. Mooney
    • 1
  • Denise de Vries
    • 1
  • John F. Roddick
    • 1
  1. 1.School of Informatics and EngineeringFlinders University of South AustraliaAdelaide

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