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Title:
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FUZZY ASSOCIATION RULE REDUCTION USING CLUSTERING IN SOM NEURAL NETWORK |
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Author(s):
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Marjan Kaedi , Mohammadali Nematbakhsh , Nasser Ghasem-aghaee |
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ISBN:
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978-972-8924-63-8 |
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Editors:
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Hans Weghorn and Ajith P. Abraham |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Fuzzy Data Mining, Rule Reduction, SOM. |
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Type:
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Short Paper |
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First Page:
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139 |
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Last Page:
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143 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The major drawback of fuzzy data mining is that after applying fuzzy data mining on the quantitative data, the number of
extracted fuzzy association rules is very huge. When many association rules are obtained, the usefulness of them will be
reduced. In this paper, we introduce an approach to reduce and summarize the extracted fuzzy association rules after
fuzzy data mining. In our approach, in first, we encode each obtained fuzzy association rule to a string of numbers. Then
we use self-organizing map (SOM) neural network iteratively in a tree structure for clustering these encoded rules and
summarizing them to a smaller collection of fuzzy association rules. This approach has been applied on a data base
containing information about 5000 employees and has shown good results. |
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