Association rules over time

Decisions made nowadays by Artificial Intelligence powered systems are usually hard for users to understand. One of the more important issues faced by developers is exposed as how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more important role. This technique could also be applied successfully for explaining the results of Association Rule Mining.This Chapter focuses on two issues: (1) How to discover the relevant association rules, and (2) How to express relations between more attributes visually. For the solution of the first issue, the proposed method uses Differential Evolution, while Sankey diagrams are adopted to solve the second one. This method was applied to a transaction database containing data generated by an amateur cyclist in past seasons, using a mobile device worn during the realization of training sessions that is divided into four time periods. The results of visualization showed that a trend in improving performance of an athlete can be indicated by changing the attributes appearing in the selected association rules in different time periods.

[1]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[2]  Antonio Gomariz,et al.  The SPMF Open-Source Data Mining Library Version 2 , 2016, ECML/PKDD.

[3]  Nilesh Patel,et al.  Evolutionary Optimization Based on Biological Evolution in Plants , 2018, KES.

[4]  Pak Chung Wong,et al.  Visualizing association rules for text mining , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[5]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[6]  Seth Flaxman,et al.  European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..

[7]  Carson Kai-Sang Leung,et al.  CloseViz: visualizing useful patterns , 2010, UP '10.

[8]  Hans Kellerer,et al.  Knapsack problems , 2004 .

[9]  Iztok Fister,et al.  Discovering dependencies among mined association rules with population-based metaheuristics , 2019, GECCO.

[10]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2015, Natural Computing Series.

[11]  Seth Flaxman,et al.  EU regulations on algorithmic decision-making and a "right to explanation" , 2016, ArXiv.

[12]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[13]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[14]  Christopher Gandrud,et al.  D3 JavaScript Network Graphs from R , 2015 .

[15]  Annalisa Appice,et al.  Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization , 2005, IEA/AIE.

[16]  Mustafa Mat Deris,et al.  SMARViz: Soft Maximal Association Rules Visualization , 2009, IVIC.

[17]  Peter J. Stuckey,et al.  Optimal Sankey Diagrams Via Integer Programming , 2018, 2018 IEEE Pacific Visualization Symposium (PacificVis).

[18]  R H Thurston,et al.  The Thermal Efficiency of Steam Engines , 1924, Nature.

[19]  Abon Chaudhuri.  A Visual Technique to Analyze Flow of Information in a Machine Learning System , 2018, Visualization and Data Analysis.

[20]  Ellis Horowitz,et al.  Fundamentals of Computer Algorithms , 1978 .

[21]  B. Lehrman.  Visualizing water infrastructure with Sankey maps: a case study of mapping the Los Angeles Aqueduct, California , 2018 .

[22]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[23]  Ee-Peng Lim,et al.  CrystalClear: Active visualization of association rules , 2002 .

[24]  Xiaohua Hu,et al.  A finite ranked poset and its application in visualization of association rules , 2008, 2008 IEEE International Conference on Granular Computing.

[25]  Heike Hofmann,et al.  Visualizing association rules with interactive mosaic plots , 2000, KDD '00.

[26]  Michael Hahsler,et al.  Visualizing association rules in hierarchical groups , 2016, Journal of Business Economics.

[27]  Om Prakash Mahela,et al.  Plant Biology-Inspired Genetic Algorithm: Superior Efficiency to Firefly Optimizer , 2019, Springer Tracts in Nature-Inspired Computing.

[28]  Iztok Fister.  Information cartography in association rule mining , 2020, ArXiv.

[29]  Mitsuhiko Toda,et al.  Methods for Visual Understanding of Hierarchical System Structures , 1981, IEEE Transactions on Systems, Man, and Cybernetics.