An Efficient Framework for Building Fuzzy Associative Classifier Using High-Dimensional Dataset

Abstract

Abstract. Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks for classification and clustering. Traditional Fuzzy ARM algorithms have failed to mine rules from high-dimensional data efficiently, since those are meant to deal with relatively much less number of attributes or dimensions. Fuzzy ARM with high-dimensional data is a challenging problem to be addressed. This paper uses a quick and economical Fuzzy ARM algorithm FAR-HD, which processes frequent item sets using a two-phased multiple-partition approach especially for large high-dimensional datasets. The proposed algorithm is an extension to the FAR-HD process in which it improves the accuracy in terms of associative soft category labels by building a framework for fuzzy associative classifier to leverage the functionality of fuzzy association rules. Fuzzy ARM represent latent and dominant patterns in the given dataset, such a classifier is anticipated to supply superb accuracy particularly in terms of fuzzy support.

Incremental Fuzzy Association Rule Mining for Classification and Regression

“…In [6], a novel associative classification model based on a fuzzy frequent pattern mining algorithm (AC-FFP) is proposed that uses the membership function to fuzzify the input variables and further mine the classification association rules based on FP-growth. An efficient mining algorithm known as fuzzy association rules for high-dimensional problems (FAR-HD) was proposed in [7], which processes frequent itemsets using a two-phased multiple-partition approach especially for large high-dimensional datasets. The FAR-HD process improves the accuracy in terms of associative soft category labels by building a framework for the fuzzy associative classifier to leverage the functionality of fuzzy association rules.…”