Chun-Wei Lin, Tzung-Pei Hong, and
Wen-Hsiang Lu
Abstract
In the past,
many algorithms were proposed for mining association rules, most of which were
based on items with binary values. In this paper, a novel tree structure called
the compressed fuzzy frequent pattern tree (CFFP tree) is designed to store the
related information in the fuzzy mining process. A mining algorithm called the
CFFP-growth mining algorithm is then proposed based on the tree structure to
mine the fuzzy frequent itemsets. Each node in the tree has to keep the membership
value of the contained item as well as the membership values of its
super-itemsets in the path. The database scans can thus be greatly reduced with
the help of the additional information. Experimental results also compare the
performance of the proposed approach both in the execution time and the number
of tree nodes at two different numbers of regions, respectively.
Keywords: fuzzy data mining,
fuzzy set, quantitative value, CFFP trees, CFFP-growth, fuzzy frequent patterns.
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