Issue:Generalized net of the process of association rules discovery by Eclat algorithm using weather databases

shortcut

http://ifigenia.org/wiki/issue:iwgn-13-1-10

Title of paper: Generalized net of the process of association rules discovery by Eclat algorithm using weather databases Author(s): Veselina Bureva "Prof. Asen Zlatarov" University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria vesito_ka@abv.bg Evdokia Sotirova Prof. Asen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria esotirova@btu.bg Presented at: 14th IWGN, Burgas, 29-30 November 2013 Published in: Conference proceedings, pages 1—10 Download:  PDF (158  Kb, File info) Abstract: In the present paper, a Generated net model is constructed to determine the possibility of forest fire by association rules. To model the process, we use frequent pattern mining by the Eclat algorithm. A pattern is considered to be frequent when it occurs in the data more often than a predefined minimum support frequency. Frequent pattern mining is a step of the process of association rules discovery. Eclat algorithm uses vertical data format for generating frequent patterns, with associative rules having the If A then B form. The proposed Generated net model should both fit well the input metrological observations, and correctly predict previously unknown weather parameters. It can be used for monitoring of the possibility of fire via frequent pattern mining depending on metrological conditions. Keywords: Generalized Net, Association rules, Weather databases, Frequent pattern mining, Data Mining, Knowledge Discovery. AMS Classification: 68Q85, 62H30. References: Agrawal, R., Imielinski T., And Swami A., Mining Association Rules Between Sets Of Items In Large Databases, in Proceedings of ACM-SIGMOD Conference, Washington, DC, 1993 Atanassov, K. Generalized Nets. World Scientific, Singapore, 1991. Atanassov, K. On Generalized Nets Theory. Prof. M. Drinov Academic Publishing House, Sofia, 2007. Bureva, V. Methods for extracting patterns from databases, Management and Education, University "Prof. Asen Zlatarov", Burgas, Vol. 8 (4), 2012, 255–258 (in Bulgarian). Bureva, V. Algorithms for associative rule mining, Management and Education, University "Prof. Asen Zlatarov", Burgas, Vol. 9 (6) 2013, 121–128 (in Bulgarian). Bureva, V. Generalized model of the process of the creating the association rules using Apriori algorithm, Annual of “Informatics” Section Union of Scientists in Bulgaria, Vo. 5, 2012, 73–83 (in Bulgarian). Bureva, V. Generalized model of the process of the creating the association rules using Frequent Pattern-Growth Method, Annual of “Informatics” Section Union of Scientists in Bulgaria, 2013 (in bulgarian, in press). Ghosh, S., Nag, A., Biswas, D., Singh, J.P., Biswas, S., Sarkar, D., Sarkar, P.P., Weather Data Mining using Artificial Neural Network, Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 2011, 192–195. TKaur, G., Meteorological Data Mining Techniques: A Survey, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 8, August 2012, 325–327. http://www.ijetae.com/files/Volume2Issue8/IJETAE_0812_56.pdf. Larose, D., Discovering Knowledge In Data. An Introduction To Data Mining, John Wiley& Sons, 2005. Tan, P., M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006. Kalyankar, M., S. Alaspurkar, Data Mining Technique to Analyse the Metrological Data, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 2, February 2013, 114–118. http://www.ijarcsse.com Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.