| Summary: | 碩士 === 元智大學 === 電資與資訊工程研究所 === 86 === On the existing algorithms for mining association rules
of data mining, the correlations between items become quite
complicated when items grow larger and larger. In that case,
the computing process will take tremendous time.
To improve the computing time for the above mention problem,
some research deal with the items through properly distinguishing,
such as (1) building hierarchical relation and (2) attribute
clustering.
In this thesis, another categorizing method is proposed.
First we distinguish the large itemsets using the characteristic
of definition of minimal confidence. Then, the processes of
generating association rules are effectively reduced based on
the above categorization. This method may improve the computing
time quite efficiently.
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