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Document Clustering Based on a Weighted Exponential Measurement

  • Shahrooz Taheri
  • Alex Tze Hiang Sim
  • Seyed Hamid Ghorashi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)

Abstract

Frequent terms sets clustering method has been proposed to overcome hardship of high dimensionality, and finding meaningful labels for clusters. Although this method provides meaningful labels for clusters, it has low accuracy. In this research, candidate clusters are extracted by mining frequent terms set within documents dataset. Each document is assigned to these clusters with considering the value of supports. A new similarity measurement function for clusters is designed based on similarity and weight of clusters and is proposed to remove unwanted clusters in a noise reduction step. The proposed method operates based on the concept of terms sets, value of support and weight of each cluster. Experimental results show that our proposed method provides more accurate clusters in comparison with previous efforts done on “Re0” and “Hitech” datasets.

Keywords

Clustering Frequent Terms Set Noise Reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Shahrooz Taheri
    • 1
  • Alex Tze Hiang Sim
    • 1
  • Seyed Hamid Ghorashi
    • 1
  1. 1.Department of Information Systems, Faculty of Computer Science and Information SystemsUnivevrsiti Teknologi MalaysiaSkudaiMalaysia

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