Using Supervised Attribute Selection for Unsupervised Learning

Irrelevant attributes in real-world data sets are known to affect data mining performance as well as making a model to become opaque and hard to interpret. In supervised learning, the problem of attribute selection is commonly solved using the Supervised Wrapper Approach (SWA). This approach is well established---it searches for an attribute-subset that gives the best predictive accuracy attained by a (wrapped) predictive model. Hence, the prerequisite for SWA to work is the availability of target variables. In unsupervised learning, target variables do not exist, and the applicability of SWA to unsupervised learning problems seems counterintuitive. This paper demonstrates how SWA can be used for attribute selection in data clustering, an important task in unsupervised learning. Experimental results show that the proposed method can be used to find relevant attributes and remove irrelevant ones, resulting in smaller data size and lower dimensionality, and improved clustering performance.