Dimensionality Reduction for Association Rule Mining

Selecting relevant features from a dataset has been considered to be one of the major components of Data Mining techniques [1], [3]. Data mining techniques become computationally expensive when used with irrelevant features. Dimensionality reduction/feature selection algorithms are used basically to reduce the dimension(s) of a dataset without reducing the information content of the domain. There are basically two categories of feature selection methods: supervised, where each instance is associated with a class label, and un-supervised, where instances are not related to any class label. Un-supervised feature selection is used as a pre-processing of other machine learning techniques such as clustering, classification or association rule mining to reduce the dimensionality of the domain space without much loss of information content. This paper presents three techniques to reduce the dimensionality of dataset that are helpful to association rule mining problem. Out of them two can be found to be useful to the classical method of rule mining that treats the rule mining problem as single objective one. Whereas, the third one has been established to be effective with multi objective association rule mining.