Title of the Paper: A Fuzzy Statistics based Method for Mining Fuzzy Correlation Rules
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Authors: Nancy P. Lin, Hung-Jen Chen, Hao-En Chueh,
Wei-Hua Hao, Chung-I Chang
Abstract: Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur
together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as
interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other
one in the same record. To prevent generating this kind of misleading fuzzy rule, in this paper, we construct a
new method for finding relationships between fuzzy itemsets based on fuzzy statistics, and the generated rules
are called fuzzy correlation rules. In our method, a fuzzy correlation analysis which can show us the strength
and the type of the linear relationship between two fuzzy itemsets is used. By using thus fuzzy statistics
analysis, the fuzzy correlation rules with the information about that two fuzzy not only frequently occur
together in same records but also are related to each other can be generated.
Keywords: Fuzzy association rules, Fuzzy itemsets, Fuzzy statistics, Fuzzy correlation analysis, Linear
relationship, Fuzzy correlation rules
Title of the Paper: Convergence of the Collocation
Methods for Singular Integrodifferential Equations in Lebesgue Spaces
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Authors: Iurie Caraus and Nikos E. Mastorakis
Abstract: In this article, the numerical schemes of collocation methods for an approximate solution of singular
integro- differential equations with kernels of Cauchy type are explained. The equations are defined on arbitrary
smooth closed contours. The theoretical background of collocations methods in Lebesgue spaces is obtained.
Keywords: singular integro-differential equations, collocation methods.