Discover Multi-Label Classification using Association Rule Mining

Abstract

Association rule mining and classification are two major task of data mining. They
are attracted wide attention in both research and application area recently. I propose a method
for classification rules from multi-label dataset using association rule analysis. Multi label
dataset contains multiple class label attribute for predict target variable. We classify that
attribute using different approaches like naviye-baies, decision tree, Back propagation,
Neural based classification and association rule based classification. Finding association rule
from dataset we have to apply various algorithms like Apriori, Fp-Growth, etc. I proposed
Fp-Growth algorithm for finding association rule from dataset because of Fp-Growth is an
improved algorithm of Apriori and Fp-Growth is more efficient than Apriori. The number of
associations present in even moderate sized databases can be, however, very large – usually
too large to be applied directly for classification purposes. Therefore, any classification
learner using association rules has to perform three major steps: Mining a set of potentially
accurate rules, evaluating and pruning rules, and classifying future instances using the found
rule set. Implementation of improved Fp-Growth algorithm gives accurate and classify rule.
This approach is more effective, accurate and efficient than other tradition algorithms.