Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules

• Autorzy: Alcala-Fde J. , Bonarini A. , Flugy-Pape N. , Herrera F.
• Języki publikacji: EN

Abstrakty

EN

DataMining is most commonly used in attempts to induce association rules fromtransaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed.

Słowa kluczowe

EN

association rules; data mining; evolutionary algorithms; genetic algorithm

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2010
• Tom: Vol. 98, nr 1
• Strony: 1--14
• Opis fizyczny: Bibliogr. 42 poz., tab.

Twórcy

autor

Alcala-Fde J.

autor

Bonarini A.

autor

Flugy-Pape N.

autor

Herrera F.

• Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071 Granada, Spain,

Bibliografia

• [1] Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases, SIGMOD,Washington D.C. (USA), May 1993.
• [2] Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules, International Conference on Very Large Data Bases, Santiago de Chile (Chile), September 1994.
• [3] Alcala-Fdez, J., Alcala, R., Gacto, M. J., Herrera, F.: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms, Fuzzy Sets and Systems, 160(7), 2009, 905-921.
• [4] Bacardit, J., Garrell, J.-M.: Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (E. Cantu-Paz, J. Foster, Eds.), vol. 2724 of Lecture Notes in Computer Science, Springer, Heidelberg, 2003, 1818-1831.
• [5] Bacardit, J., Goldberg, D.-E., Butz, M.-V.: Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule, in: Learning Classifier Systems (T. Kovacs, X. Llor, K. Takadama, P. L. Lanzi, W. Stolzmann, , S. W. Wilson, Eds.), vol. 4399 of Lecture Notes in Computer Science, Springer, Heidelberg, 2007, 291-307.
• [6] Bodon, F.: A trie-based APRIORI implementation for mining frequent item sequences, 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, Chicago, Illinois, USA, August 2005.
• [7] Borgelt, C.: Efficient Implementations of Apriori and Eclat, in: Workshop on Frequent Itemset Mining Implementations, vol. 90, CEUR Workshop Proc., Florida, USA, 2003, 280-296.
• [8] Chen, C.-H., Hong, T.-P., Tseng, V. S., Lee, Y.-C.: A genetic-fuzzy mining approach for items with multiple minimum supports, Soft Computing, 13(5), 2008, 521-533.
• [9] Cordón,O., Herrera, F., Hoffmann, F.,Magdalena, L.: GENETIC FUZZY SYSTEMS. Evolutionary tuning and learning of fuzzy knowledge bases. Advances in Fuzzy Systems - Applications and Theory, World Scientific, 2001.
• [10] Eiben, A. E., Smith, J. E.: Introduction to Evolutionary Computing, SpringerVerlag, 2003.
• [11] Freitas, A. A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2002.
• [12] Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.
• [13] Greene, D., Smith, S.: Competition-based induction of decision models from examples, Machine Learning, 13(2-3), 1993, 229-257.
• [14] Grefenstette, J. J.: Genetic Algorithms for Machine Learning, Kluwer Academic Publishers, Norwell, MA, USA, 1994.
• [15] Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition, Morgan Kaufmann, San Fransisco, 2006.
• [16] Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, 8(1), 2004, 53-87.
• [17] Herrera, F.: Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects, Evolutionary Intelligence, 1(1), 2008, 27-46.
• [18] Holland, J., Reitman, J.: Cognitive systems based on adaptive algorithms, in: Patter-directed inference systems (D. Waterman, F. Hayes-Roth, Eds.), Academic Press, London, 1978, 1148-1158.
• [19] Holland, J. H.: Adaptation in natural and artificial systems, MIT Press, Cambridge,MA, USA, 1975.
• [20] Hong, T.-P., Chen, C.-H., Lee, Y.-C., , Wu, Y.-L.: Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy, IEEE Transactions on Evolutionary Computation, 12(2), 2008, 252-265.
• [21] Jain, L. C., Ghosh, A.: Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing), Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.
• [22] Janssens, D., Brijs, T., Vanhoof, K.,Wets, G.: Evaluating the performance of cost-based discretization versus entropy- and error-based discretization, Computers and Operations Research, 33(11), 2006, 3107-3123.
• [23] Jong, K. D., Spears, W., Gordon, D.: Using genetic algorithms for concept learning, Machine Learning, 13(2-3), 1993, 161-188.
• [24] Kaya, M., Alhajj, R.: Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining, Applied Intelligence, 24(1), 2006, 7-15.
• [25] Lee, C.-H.: A Hellinger-based discretization method for numerica attributes in classification learning, Knowledge-Based Systems, 20(4), 2007, 419-425.
• [26] Liu, H., Hussain, F., Tan, C., Dash,M.: Discretization: An Enabling Technique, DataMining and Knowledge Discovery, 6(4), 2002, 393-423.
• [27] Mata, J., Alvarez, J., Riquelme, J.: Discovering Numeric Association Rules via Evolu-tionary Algorithm, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (G. Rawlin, Ed.), vol. 2336 of Lecture Notes in Computer Science, Springer, Heidelberg, 1991, 40-51.
• [28] Mata, J., Alvarez, J., Riquelme, J.: Mining Numeric Association Rules with Genetic Algorithms, 5th International Conference on Artificial Neural Networks and Genetic Algorithms, Taipei, Taiwan, April 2001.
• [29] Mata, J., Alvarez, J., Riquelme, J.: An Evolutionary Algorithm to Discover Numeric Association Rules, ACM Symposium on Applied Computing,Madrid, Spain, March 2002.
• [30] Mladenic, D., Lavrac, N., Bohanec, M., Moyle, S., Eds.: Data Mining and Decision Support: Integration and Collaboration, Kluwer Academic Publishers, Norwell, MA, USA, 2002.
• [31] Pal, S. K., Wang, P. P., Eds.: Genetic Algorithms for Pattern Recognition, CRC Press, Inc., Boca Raton, FL, USA, 1996.
• [32] Smith, S.: A learning system based on genetic algorithms, Ph.D. Thesis, University of Pitts-burgh, 1980.
• [33] Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables, ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 1996.
• [34] T-Kovacs: Strength or accuracy: credit assignment in learning classifier systems, Springer-Verlag, Berlin, 2004.
• [35] Tsai, C.-J., Lee, C.-I., Yang,W.-P.: A discretization algorithm based on Class-Attribute Contingency Coefficient, Information Science, 178(3), 2008, 714-731.
• [36] Tsumoto, S., Matsuoka, K., Yokoyama, S.: Risk Mining for Infection Control, in: Communications and Discoveries from Multidisciplinary Data (S. Iwata, Y. Ohsawa, S. Tsumoto, N. Zhong, Y. Shi, L. Magnani, Eds.), vol. 123 of Studies in Computational Intelligence, Springer, Heidelberg, 2008, 283-297.
• [37] Venturini, G.: SIA: a supervised inductive algorithm with genetic search for learning attrib-ute based concepts, in: Proceedings of the European Conference on Machine Learning, vol. 667 of Lecture Notes in Computer Science, Viena, Austria, April 1993, 280-296.
• [38] Wong, M. L., Leung, K. S.: Data Mining Using Grammar-Based Genetic Programming and Applications, Kluwer Academic Publishers, Norwell, MA, USA, 2000.
• [39] Yan, X., Zhang, C., Zhang, S.: ARMGA: Identifying Interesting Association Rules with Genetic Algorithms, Applied Artificial Intelligence, 19(7), 2005, 677-689.
• [40] Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support, Expert Systems with Applications, 36(2), 2009, 3066-3076.
• [41] Zaki, M. J.: Scalable Algorithms for Association Mining, IEEE Transactions on Knowledge and Data Engineering, 12(3), 2000, 372-390.
• [42] Zhang, C., Zhang, S.: Association Rule Mining: Models and Algorithms Series, Lecture Notes in Computer Science, LNAI 2307, Springer-Verlag, Berlin, 2002