WorldCIST'13 -The 2013 World Conference on Information Systems and Technologies

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Multilevel Clustering of Induction Rules for Web Meta-Knowledge

Amine Chemchem

Habiba Drias

Youcef Djenouri

The current world wide web is featured by a huge mass of
knowledge, making it difficult to exploit. One possible way to cope with
this issue is to proceed to knowledge mining in a way that we could
control its volume and hence make it manageable. This paper explores
meta-knowledge discovery and in particular focuses on clustering induction
rules for large knowledge sets. Such knowledge representation is
considered for its expressive power and hence its wide use. Adapted data
mining is proposed to extract meta knowledge taking into account the
knowledge representation which is more complex than simple data. Besides,
a new clustering approach based on multilevel paradigm and called
multilevel clustering is developed for the purpose of treating large scale
knowledge sets. The approach invokes k-means algorithm to cluster induction
rules using new designed similarity measures. The developed
algorithms have been implemented on four public benchmarks to test
the effectiveness of the multiclustering approach. The numerical results
have been compared to those of the simple k-means algorithm. As foreseeable,
the multilevel clustering outperforms clearly the basic k-means
on both the execution time and success rate that remains constant to
100 % while increasing the number of induction rules.


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