Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications

Abstract

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…It has furthermore been suggested that parallel coordinates cannot display certain kinds of information and should be complemented (e.g., with spatial representations) for a more complete representation of alternatives (Xiao et al, 2007). Kok (1986), Sato et al (2015) and Bandaru et al (2017b) point out that when DMs only have a vague understanding of their preferences, it may be easier to specify loose ranges of preferences in the objective space, rather than exact points of preferences. The action of "brushing" available in interactive parallel coordinates charts addresses this issue.…”

“…The user can filter the polylines to display only those of interest by "brushing" the desired axes (Heinrich and Weiskopf, 2013;Bandaru et al, 2017b). The ability to display only the solutions which satisfy desired values on the different axes is a common response to the problem of cluttering, which causes parallel coordinates to become unreadable when too many lines are present (Johansson and Forsell, 2016;Li et al, 2017).…”

Enhancing discrete-event simulation with big data analytics: A review

“…identifying new market segments of customers or detecting e-commerce fraud). Data mining techniques found in the review include Self-Organising Maps (SOM) (Kohonen, 1995) which provide a visual map of data dependencies and Flexible Pattern Mining (FPM) (Bandaru et al, 2017) which aims at extracting patterns of rules within a given data set.…”

A framework for simulation-based multi-objective optimization and knowledge discovery of machining process

“…In this module, we use the recently proposed flexible pattern mining (FPM) [35] approach to extract rules that distinguish the obtained trade-off solutions from the other feasible solutions obtained during the optimization process. FPM works on the same principle as frequent itemset mining that is used often on market basket data.…”