Multi-objective association rule mining with binary bat algorithm

Abstract

Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algorithms such as genetic algorithm is slow and the efficiency of these algorithms is low. Furthermore, the rules generated by traditional multi-objective algorithms are too large to be efficiently analyzed and explored in any further process. Bat algorithm is a new efficient global optimal algorithm whose convergence is superior to binary particle swarm optimization (BPSO) and genetic algorithm. This paper discusses the application of multi-objective bat algorithm to association rule mining. We propose multi-objective binary bat algorithm (MBBA) based on Pareto for association rule mining. This algorithm is independent of minimum support and minimum confidence. To evaluate the association rules mined by MBBA algorithm, we propose a new method to discover interesting association rules without favoring or excluding any measure. Compared with the single-objective BPSO, binary bat algorithm (BBA) and Apriori algorithm, the experimental results on six datasets show that the new algorithm is feasible and highly effective. It can make up the shortage of single objective algorithms and traditional association rule mining algorithms.

Keywords

Association rule mining bat algorithm multi-objective Pareto fronter

References

Agrawal

, Imieliński

and Swami

, Mining association rules between sets of items in large databases, in: Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, Washington, DC, USA: ACM Press (1993), 207-216.

Agrawal

and Srikant

, Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Database, Morgan Kaufm An Pub Inc (1994), 487-489.

Han

, Pei

and Yin

et al., An frequent patterns without candidate generation: A frequent-pattern tree approach, Pruc of ACM SIGMOD Intemational Conference on Management of Data, New York: ACM Press (2000), 1-12.

Yang

X.S.

and Deb

, Engineering optimization by cuckoo search, International Journal of Mathematical Modeling and Numerical 1(4) (2010), 330-343.

Z.-D.

, You

F.C.

and Yang

B.R.

, Comprehensive evaluation method of association rules and an practical example, Computer Application 24(10) (2004), 17-20.

Qin

, Novelty evaluation of association rules, The Research and Application of Computer 1 (2004), 17-19.

Saggar

, Agrawal

A.K.

and Lad

, Optimization of association rule mining using improved genetic algorithms, in: Proceeding of the IEEE International Conference on Systems Man and Cybernetics 4 (2004), 3725-3729.

Sarath

and Ravi

, Association rule mining using binary particle swarm optimization, Engineering Applications of Artificial Intelligence 26 (2013), 1832-1840.

Waiswa

P.P.

and Baryamureeba

, Extraction of interesting association rules using genetic algorithms, Int J Comput Ict Res 2(1) (2008), 26-33.

10.

Hadian

and Nasiri

A.M.

, Clustering based multi-objective rule mining using genetic algorithm, Int J Digital Content Technol 4 (2010), 37-42.

11.

Alatas

and Akin

, Multi-objective differential evolution algorithm for mining numeric association rules, Appl Soft Compute (2008), 646-656.

12.

Alatas

and Akin

, Rough particle swarm optimization and its applications in data mining, Soft Computing-A Fusion of Foundations, Methodologies and Applications 12 (2012), 1205-1218.

13.

Alatas

and Akin

, Chaotically encoded particle swarm optimization algorithm and its applications, {Chaos}, Solitons and Fractals 41 (2009), 939-950.

14.

Kuo

R.J.

, Chao

C.M.

and Chiu

Y.T.

, Application of particle swarm optimization to association rule mining, Appl Soft Comput (2011), 326-336.

15.

Gupta

, Application of weighted particle swarm optimization in association rule mining, Int J Comput Sci Inf 1 (2012), 2231-5292.

16.

Asadi

, Afzali

, Shojaei

and Sulaimani

, A new binary PSO based method for finding best thresholds in association rule mining, Life Science Journal (2012), 260-264.

17.

Yang

X.-S.

, A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization, Springer: Studies in Computational Intelligence (2010), 65-74.

18.

Gandomi

A.H.

, Yang

X.-S.

, Alavi

A.H.

et al., Bat algorithm for constrained optimization tasks, Neural Comput and Applic, doi: 101007/s00521-012-1028-9.

19.

Kennedy

and Eberhart

R.C.

, A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems Man and Cybernetics, Piscataway: IEEE (1997), 4104-4109.

20.

Liu

, The research of basic theory and improvement on particle swarm optimization, Changsha: Central South University, 2007.

21.

Ballesteros

M.M.

and Álvarez

F.M.

, Selecting the best measures to discover quantitative association rules, Neuro Computing (2013), Http://dxdoi.org/10.1016/j.neucom.2013.01.056.

22.

Tsa

L.C.

, and Sun

T.Y.

, An improved multi-objective particle swarm optimizer for multi-objective problems, Expert System swith Applications 37(8) (2010), 5872-5886.

23.

Deb

, Pratap

, Agarwal

and Meyarivan

, A fast and elitist multi-objective genetic algorithm, NSGA-IIIEEE Trans on Evolutionary Computation 6(2) (2002), 182-197.