Abstract
The Process of finding out correlations among the data items in the databases forms the core concept in Association Rule Mining. The Association rule Mining algorithms helps in decision making process. Since it plays a vital role in this area, the rules generated by the algorithms should be of less in number and precise. Association rule algorithms, such as Apriori, scrutinize a long list of transactions in order to decide which items are most commonly purchased together. The challenge of digging out association patterns from data draws upon research in databases, machine learning and optimization to bring advanced intelligent solutions. But even though it provides some robustness, the rules generated from the algorithm may be redundant in some cases. So in order to overcome the problems we need to optimize the rules generated from these algorithms. Here we consider the Utility based Temporal Association Rule Mining method for generating the association rules and the Particle Swam Optimization algorithm is used to optimize the generated rules. The main processes in this proposed approach are calculation of the support and confidence from the input data, the Rule generation, Initialization , updation of the velocity , position of the rules and evaluation of fitness function. This paper attempts to use the PSO technique to optimize the utility based temporal rules by filtering out the redundant rules and thereby reducing the problem space.
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References
Dewang, R., Agarwal, J.: A New Method for Generating All Positive and Negative Association Rules. International Journal on Computer Science and Engineering (IJCSE) 3(4), 1649–1657 (2011)
Olafsson, S., Li, X., Wu, S.: Operations research and data mining. European Journal of Operational Research 187(3), 1429–1448 (2008)
Agrawal, R., Imielinksi, T., Swami, A.: Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering, 914–925 (1993)
Arunadevi, J., Rajamani, V.: Optimization of Spatial Association Rule Mining using Hybrid Evolutionary algorithm. International Journal of Computer Applications 1(19), 86–89 (2010)
Ykhlef, M.: A Quantum Swarm Evolutionary Algorithm for mining association rules in large databases. Journal of King Saud University – Computer and Information Sciences 23, 1–6 (2011)
Agrawal, R., Imielinski, T., Swami, S.: Mining association rules between sets of items in large databases, pp. 207–216 (1993a)
Agrawal, R., Imielinski, T., Swami, S.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington DC, pp. 207–216 (1993b)
Angiulli, F., Ianni, G., Palopoli, L.: On the complexity of mining association rules. In: Proceedings of SEBD, pp. 177–184 (2001)
Mangat, V.: Swarm Intelligence Based Technique for Rule Mining in the Medical Domain. International Journal of Computer Applications 4(1), 19–24 (2010)
Kotsiantis, S., Kanellopoulos, D.: Association Rules Mining: A Recent Overview. GESTS International Transactions on Computer Science and Engineering 32(1), 71–82 (2006)
Hegland, M.: Algorithms for Association Rules. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 226–234. Springer, Heidelberg (2003)
Pedersen, M.E.H., Chipperfield, A.J.: Simplifying Particle Swarm Optimization. Applied Soft Computing, 1–32 (2009)
Lee, K.Y.: Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages. In: IEEE PSCE (2006)
Zemirline, A., Lecornu, L., Solaiman, B., Ech-Cherif, A.: An Efficient Association Rule Mining Algorithm for Classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 717–728. Springer, Heidelberg (2008)
Qodmanan, H.R., Nasiri, M., Minaei-Bidgoli, B.: Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. An International Journal Expert Systems with Applications 38(1), 288–298 (2011)
Bellandi, A., Furletti, B., Grossi, V., Romei, A.: Ontological support for Association Rule Mining. In: Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications, pp. 110–115 (2008)
Wang, Z., Sun, X., Zhang, D.: A PSO-Based Classification Rule Mining Algorithm. In: Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications, pp. 377–384 (2009)
Kuo, R.J., Chao, C.M., Chiu, Y.T.: Application of particle swarm optimization to association rule mining. Applied Soft Computing 11(1), 326–336 (2011)
Ykhlef, M.: A Quantum Swarm Evolutionary Algorithmfor mining association rules in large databases. Journal of King Saud University – Computer and Information Sciences 23, 1–6 (2011)
Mangat, V.: Swarm Intelligence Based Technique for Rule Mining inthe Medical Domain. International Journal of Computer Applications 4(1), 19–24 (2010)
Wang, H.-S., Yeh, W.-C., Huang, P.-C., Chang, W.-W.: Using association rules and particle swarm optimization approach for part change. International Journal of Expert Systems with Applications 36(4), 8178–8184 (2009)
Carvalho, A., Pozo, A.: Non-Ordered Data Mining Rules Through Multi-Objective Particle Swarm Optimization: Dealing with Numeric and Discrete Attributes. In: Proceedings of the Eighth International Conference on Hybrid Intelligent Systems, pp. 495–500 (2008)
Cai, G.-R., Li, S.-Z., Chen, S.-L.: Mining Fuzzy Association Rules by Using Nonlinear Particle Swarm Optimization. AISC, vol. 82, pp. 621–630 (2010)
Maragatham, G., Lakshmi, M.: A Strategy for Mining Utility based Temporal Association Rules. In: Proceedings of the 2nd International Conference on Trendz in Information Sciences and Computing, TISC-2010 Organized by Sathyabama University in association with IEEE and Cognizant (2010)
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Maragatham, G., Lakshmi, M. (2012). A Weighted Particle Swarm Optimization Technique for Optimizing Association Rules. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds) Global Trends in Information Systems and Software Applications. ObCom 2011. Communications in Computer and Information Science, vol 270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29216-3_71
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DOI: https://doi.org/10.1007/978-3-642-29216-3_71
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