SSDP+: A Diverse and More Informative Subgroup Discovery Approach for High Dimensional Data

This paper presents an evolutionary approach for mining diverse and more informative subgroups focused on high dimensional data sets. Subgroup Discovery (SD) is an important tool for knowledge discovery that aims to identify sets of features that distinguish a target group from the others (e.g. successful from unsuccessful treatments). At the same time, to extract information from high dimensional data sets becomes more natural. One of the first and most efficient SD heuristics focused on high dimensional data is the SSDP. However, this model deals superficially with diverse/redundancy in top-k subgroups, which can result in poor information for users. This work presents SSDP+, an extension of the SSDP model to provide diversity in a way that explore the relation between subgroups order to 2enerate a more informative set of patterns.