An Evolutionary Algorithm to Mine High-Utility Itemsets

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs) based on the binary particle swarm optimization. A maximal pattern (MP)-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

[1]  Mengchi Liu,et al.  Mining high utility itemsets without candidate generation , 2012, CIKM.

[2]  Yue-Shi Lee,et al.  Mining High Utility Quantitative Association Rules , 2007, DaWaK.

[3]  Mounir Boukadoum,et al.  Particle swarm classification: A survey and positioning , 2013, Pattern Recognit..

[4]  Tzung-Pei Hong,et al.  An effective tree structure for mining high utility itemsets , 2011, Expert Syst. Appl..

[5]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[6]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[7]  Ansaf Salleb-Aouissi,et al.  QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules , 2007, IJCAI.

[8]  Howard J. Hamilton,et al.  Mining itemset utilities from transaction databases , 2006, Data Knowl. Eng..

[9]  Vincent S. Tseng,et al.  FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning , 2014, ISMIS.

[10]  Franz Oppacher,et al.  Techniques for evolutionary rule discovery in data mining , 2009, 2009 IEEE Congress on Evolutionary Computation.

[11]  Ying Liu,et al.  A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets , 2005, PAKDD.

[12]  Raj P. Gopalan,et al.  Efficient Mining of High Utility Itemsets from Large Datasets , 2008, PAKDD.

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[16]  Minrui Fei,et al.  A Novel Hybrid Binary PSO Algorithm , 2011, ICSI.

[17]  Cory J. Butz,et al.  A Foundational Approach to Mining Itemset Utilities from Databases , 2004, SDM.

[18]  Kandhasamy Premalatha,et al.  Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation , 2014, Appl. Artif. Intell..

[19]  Tzung-Pei Hong,et al.  An efficient projection-based indexing approach for mining high utility itemsets , 2012, Knowledge and Information Systems.

[20]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[21]  Vadlamani Ravi,et al.  Association rule mining using binary particle swarm optimization , 2013, Eng. Appl. Artif. Intell..