Mining Actionable Subspace Clusters in Sequential Data

Extraction of knowledge from data and using it for decision making is vital in various real-world problems, particularly in the financial domain. We identify several financial problems, which require the mining of actionable subspaces defined by objects and attributes over a sequence of time. These subspaces are actionable in the sense that they have the ability to suggest profitable action for the decision-makers. We propose to mine actionable subspace clusters from sequential data, which are subspaces with high and correlated utilities. To efficiently mine them, we propose a framework MASC (Mining Actionable Subspace Clusters), which is a hybrid of numerical optimization, principal component analysis and frequent itemset mining. We conduct a wide range of experiments to demonstrate the actionability of the clusters and the robustness of our framework MASC. We show that our clustering results are not sensitive to the framework parameters and full recovery of embedded clusters in synthetic data is possible. In our case-study, we show that clusters with higher utilities correspond to higher actionability, and we are able to use our clusters to perform better than one of the most famous value investment strategies.