Trend Mining 2.0: Automating the Discovery of Variable Trends in the Objective Space

Updated: Jun 24

Henrik Smedberg, Sunith Bandaru, Amos H.C. Ng & Kalyanmoy Deb (2020). Trend Mining 2.0: Automating the Discovery of Variable Trends in the Objective Space. Conference paper: 2020 IEEE Congress on Evolutionary Computation, CEC 2020.


Practical multi-criterion decision making not only involves the articulation of preferences in the objective space, but also a consideration of how the variables impact these preferences. Trend mining is a recently proposed visualization technique that offers the decision maker a quick overview of the variables' effect on the structure of the objective space and easily discover interesting variable trends. The original trend mining approach relies on a set of predefined reference directions along which an interestingness score is measured for each variable. In this paper, we relax this requirement by automating the approach to find optimal reference directions that maximize the interestingness for each variable. Additional extensions include the use of an Achievement Scalarizing Function (ASF) for ranking solutions along a given reference direction, and an updated interestingness score formulation for more appropriately handling discrete variables. We demonstrate the working of the extended approach on DTLZ2 and WFG2 benchmarks for up to five objectives and on a biobjective engineering design problem. The results show that the ability of the proposed approach to detect variable trends in high dimensional objective spaces is heavily dependent on the quality of the solutions used. Access publication.