21-05-2014, 02:16 PM
[color=#FFE0]7. Optimized frequent Pattern Mining for Classified data set[/color]
ABSTRACT
This paper is based on mining frequent patterns from data sets. In this
modified Apriori algorithm is presented for discovering frequent items in data
sets that are classified into categories, assuming that a transaction involves
maximum one item being picked up from each category. The specialized
algorithm takes less time for processing on classified data sets by optimizing
candidate generation. In the paper it also showed that, for the same number of
data items, the optimized algorithm performs better when the items are
distributed under more categories. More importantly, the proposed method can
be used for a more efficient mining of relational data bases. Future work would
focus on applying Association Rule mining techniques to enhance Web
services and Web mining with better data management in the area of storage
and search facilities where the techniques have a lot of potential.