Book Details

A Recent Approach on Identifying the ARM Based Clustering Algorithm on Text and Image Context

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).

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Abstract

Each and every association rule has an aid and a confidence. Association Rules (AR) can also considered as itemset. Association Rule Mining (ARM) gets into the new technological fields. The fields consist of Classification Association Rule Mining (CARM) and distributed ARM. Now a day’s work in progress for developing faster execution of ARM algorithm which will reduce the execution time. Text mining is dealt with the application of data mining techniques to document sets. Image Association Rule Mining is involved with the application of Association Rule Mining techniques to image collections. Here large number of textual data contains many itemsets. If we mined the itemsets many times in short interval of time it reduce the dimensionality of the given text document extremely. To get the meaningful information for the given text document many data mining techniques used. This technique contains clustering. Text clustering means a group of documents into many groups.

References

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[7] Hany Mahgoub, Dietmar Rosner, Nabil Ismail and Fawzy Torkey, "A Text Mining Technique Using Association Rules Extraction", International Journal of Computational Intelligence, Vol. 4; No. 1, 2008

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[9] Association Rule Mining in The Wider Context of Text, Images and Graphs by F Coenen Department of Computer Science, The University of Liverpool.

[10] A Text Mining Technique Using Association Rules Extraction by Hany Mahgoub, Dietmar Rösner, Nabil Ismail and Fawzy Torkey.

Keywords

Association Rule Mining (ARM), Classification Association Rule Mining (CARM), Text mining, Image ARM, Text Clustering.

Image
  • Format Volume 1, Issue 2, No 4, 2013.
  • Copyright All Rights Reserved ©2013
  • Year of Publication 2013
  • Author Dr. T. Karthikeyan, J. Mohana Sundaram, R. Karthik Raj
  • Reference IJCS-021
  • Page No 111-115

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