A brief overview of swarm intelligence-based algorithms for numerical association rule mining

Numerical Association Rule Mining is a popular variant of Association Rule Mining, where numerical attributes are handled without discretization. This means that the algorithms for dealing with this problem can operate directly, not only with categorical, but also with numerical attributes. Until recently, a big portion of these algorithms were based on a stochastic nature-inspired population-based paradigm. As a result, evolutionary and swarm intelligence-based algorithms showed big efficiency for dealing with the problem. In line with this, the main mission of this chapter is to make a historical overview of swarm intelligence-based algorithms for Numerical Association Rule Mining, as well as to present the main features of these algorithms for the observed problem. A taxonomy of the algorithms was proposed on the basis of the applied features found in this overview. Challenges, waiting in the future, finish this paper.

[1]  Behrouz Minaei-Bidgoli,et al.  Mining numerical association rules via multi-objective genetic algorithms , 2013, Inf. Sci..

[2]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[3]  Hidetaka Nambo,et al.  Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution , 2019 .

[4]  Ethem Alpaydin,et al.  Machine Learning: The New AI , 2016 .

[5]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[6]  Yang Song,et al.  An Overview of Microsoft Academic Service (MAS) and Applications , 2015, WWW.

[7]  Erhan Akin,et al.  Multi-objective rule mining using a chaotic particle swarm optimization algorithm , 2009, Knowl. Based Syst..

[8]  Erhan Akin,et al.  Rough particle swarm optimization and its applications in data mining , 2008, Soft Comput..

[9]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[10]  R. J. Kuo,et al.  Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining , 2019, Neural Computing and Applications.

[11]  SuganthanPonnuthurai Nagaratnam,et al.  Computational intelligence in sports , 2015 .

[12]  Iztok Fister,et al.  Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes , 2018, IDEAL.

[13]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[14]  D. Chalmers.  The Singularity: a Philosophical Analysis , 2010 .

[15]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[16]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[17]  Azuraliza Abu Bakar,et al.  Multi-objective PSO algorithm for mining numerical association rules without a priori discretization , 2014, Expert Syst. Appl..

[18]  Bhabesh Nath,et al.  Multi-objective rule mining using genetic algorithms , 2004, Inf. Sci..

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

[20]  Habiba Drias,et al.  Multi-objective bat algorithm for mining numerical association rules , 2018, Int. J. Bio Inspired Comput..

[21]  Hidetaka Nambo,et al.  Predicting Human Position Using Improved Numerical Association Analysis for Bioelectric Potential Data , 2019 .

[22]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[23]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[24]  Pei-wei Tsai,et al.  Cat Swarm Optimization , 2006, PRICAI.

[25]  Bilal Alatas,et al.  MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules , 2008, Appl. Soft Comput..

[26]  Afshin Salajegheh,et al.  Multi-objective Numeric Association Rules Mining via Ant Colony Optimization for Continuous Domains without Specifying Minimum Support and Minimum Confidence , 2011 .

[27]  Elif Varol Altay,et al.  Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining , 2019, Journal of Ambient Intelligence and Humanized Computing.

[28]  Anne Lauscher.  Life 3.0: being human in the age of artificial intelligence , 2019, Internet Histories.

[29]  Xin-She Yang,et al.  Nature-Inspired Optimization Algorithms: Challenges and Open Problems , 2020, J. Comput. Sci..

[30]  Hidetaka Nambo,et al.  The Combination of Evolutionary Algorithm Method for Numerical Association Rule Mining Optimization , 2017 .

[31]  I. Halcu,et al.  Converting unstructured and semi-structured data into knowledge , 2013, 2013 11th RoEduNet International Conference.

[32]  S. Schneider.  Science fiction and philosophy : from time travel to superintelligence , 2016 .

[33]  Hod Lipson,et al.  Driverless: Intelligent Cars and the Road Ahead , 2016 .

[34]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[35]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.