A multi-strategy approach for mining multimedia data repositories

This paper introduces a multi-strategy learning environment, consisting of a number of heterogeneous classifiers and cluster analysis techniques, used for the mining of so-called integral data records containing 3-D objects, together with a large number of relational attributes. In this multi-strategy learning approach, the two data types are mined together, thus focusing on the interrelationships between them and utilizing their unique characteristics in a synergic way. Experimental results, when applying this approach to an anthropometric database, which contains 3-D human body scans together with (relational) demographic and anthropometric attributes, show promising results. Through the use of cluster analysis and information fusion, a streamlined grouping of human subjects, based on clothing size, was obtained. In addition, the demographic data of each cluster was explored by means of a classifier in order to obtain new insights into the population.