Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks
DOI:
https://doi.org/10.19153/cleiej.22.3.2Keywords:
Association rules, Networks, Association rules networks, Data mining, Graphs, Objective measuresAbstract
In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules in order to construct candidate hypotheses. The Filtered-ARN algorithm selects association rules with the use of asymmetric objective measures, Added Value and Gain then builds a network allowing more exploration information. The Filtered-ARN was validated using three datasets: Lenses, Hayes-roth, and Soybean Large, available online. We carried out a concept proof experiment using a real dataset with data on organic fertilization (Green Manure) for text the proposed method. The results were validated by comparing the Filtered-ARN with the conventional ARN and also comparing the results with the decision tree. The approach presented promising results, showing its ability to explain a set of objective items and the aid to build more consolidated hypotheses by guaranteeing statistical dependence with the use of objective measures.
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[34] D. B. Calçada, S. O. Rezende, and M. S. Teodoro, \Analysis of decomposition parameters of green manure in the Brazilian Northeast with Association Rules Networks," in I International Conference on Agro BigData and Decision Support Systems in Agriculture, Montevideo, Uruguay, 2017, pp. 63{65.
[2] M. Vinaya and K. Shah, \Performance Evaluation of Distributed Association Rule Mining Algorithms,"Procedia - Procedia Computer Science, vol. 79, pp. 127{134, 2016.
[3] R. Agrawal, T. Imielinski, and A. Swami, \Mining Association Rules between Sets of Items in LargeDatabases," Special Interest Group on Management of Data, 22(2), vol. 22, no. 2, pp. 207{216, 1994.
[4] R. Agrawal and R. Srikant., \Fast algorithms for mining association rules," Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proceedings of Twentieth International Conference on Very Large Data Bases (VLDB), pp. 487{499, 1994.
[5] R. Agrawal and J. C. Shafer, \Parallel mining of association rules," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962{969, 1996.
[6] M. Martínez-Ballesteros, F. Martínez-Álvarez, A. Troncoso, and J. C. Riquelme, \Selecting the best measures to discover quantitative association rules," Neurocomputing, vol. 126, pp. 3{14, 2014.
[7] C. Kim, H. Lee, H. Seol, and C. Lee, \Identifying core technologies based on technological cross-impacts: An association rule mining (ARM) and analytic network process (ANP) approach," Expert Systems with Applications, vol. 38, no. 10, pp. 12 559{12 564, 2011.
[8] G. Pandey, S. Chawla, S. Poon, B. Arunasalam, and J. G. Davis, \Association Rules Network: Definition and Applications," Statistical Analysis and Data Mining, vol. 1, no. 4, pp. 260{179, 2009.
[9] T. Le and B. Vo, \The lattice-based approaches for mining association rules: a review," Wiley Inter-disciplinary Reviews: Data Mining and Knowledge Discovery, vol. 6, no. 4, pp. 140{151, 2016.
[10] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2012.
[11] P. Fournier-Viger, J. C. W. Lin, B. Vo, T. T. Chi, J. Zhang, and H. B. Le, \A survey of itemset mining," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 1{18, 2017.
[12] C. C. Aggarwal, C. Procopiuc, and P. S. Yu, \Finding localized associations in market basket data," IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 1, pp. 51{62, 2002.
[13] L. T. T. Nguyen and N. T. Nguyen, \Updating mined class association rules for record insertion," Applied Intelligence, vol. 42, no. 4, pp. 707{721, 2015.
[14] M. A. Domingues and S. O. Rezende, \Post-processing of Association Rules using Taxonomies," Proceedings of the 12th Portuguese Conference on Artificial Intelligence (EPIA 2005), pp. 192{197, 2005.
[15] D. J. Prajapati, S. Garg, and N. C. Chauhan, \MapReduce Based Multilevel Consistent and Inconsistent Association Rule Detection from Big Data Using Interestingness Measures," Big Data Research, vol. 9, pp. 18{27, 2017.
[16] S. Brin, R. Motwani, J. D. Ulman, and S. Tsur, \Dynamic itemset counting and implication rules for market basket data," Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 255{264, 1997.
[17] G. Piatetsky-Shapiro, \Discovery, Analysis and Presentation of Strong Rules," Knowledge Discovery in Databases, AAAI/MIT Press, pp. 229{248, 1991.
[18] B. Liu, W. Hsu, and Y. Ma, \Pruning and summarizing the discovered associations," Proceedings of the 5th ACM International Conference on Knowledge Discovery and Data mining, pp. 125{134, 1999.
[19] P.-N. Tan, M. Steinbach, and V. Kumar, \Association Analysis: Basic Concepts and Algorithms," in Introduction to Data mining, 2005, pp. 327{414.
[20] S. Sahar, \What Is Interesting: Studies on Interestingness in Knowledge Discovery," Phd Thes, Tel-Aviv University The, 2003.
[21] T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, \Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization," in SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data, 1996, pp. 13{23.
[22] M. Hahsler and R. Karpienko, \Visualizing association rules in hierarchical groups," Journal of Business Economics, vol. 87, no. 3, pp. 317{335, 2017.
[23] H. Deng, G. Runger, E. Tuv, and W. Bannister, \CBC: An associative classifier with a small number of rules," Decision Support Systems, vol. 59, no. 1, pp. 163{170, 2014.
[24] E. H. Kim, H. G. Kim, S. H. Hwang, and S. I. Lee, \FARM: An FCA-based Association Rule Miner," Knowledge-Based Systems, vol. 85, pp. 277{297, 2015.
[25] M. Rashid, I. Gondal, and J. Kamruzzaman, \Mining Associated Patterns from Wireless Sensor Networks," IEEE TRANSACTIONS ON COMPUTERS, vol. 64, no. 7, pp. 1998{2011, 2015.
[26] R. d. Padua, S. O. Rezende, and V. O. D. Carvalho, \Post-processing association rules using networks and transductive learning," Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, pp. 318{323, 2014.
[27] S. Chawla, \Feature Selection, Association Rules Network and Theory Building." JMLR: Workshop and Conference Proceedings - The Fourth Workshop on Feature Selection in Data Mining, vol. 10, pp. 14{21, 2010.
[28] G. Grahne and J. Zhu, \Fast algorithms for frequent itemset mining using FP-trees," IEEE Trans Knowl Data Eng, vol. 17, pp. 1347{1362, 2005.
[29] D. F. Nettleton, \Data mining of social networks represented as graphs," Computer Science Review, vol. 7, no. 1, pp. 1{34, 2013.
[30] J. C. Valverde-Rebaza and A. De Andrade Lopes, \Link prediction in online social networks using group information," Ph.D. dissertation, Universidade de S~ao Paulo, 2014.
[31] M. Newman, Networks: An introduction, 1st ed. New York, USA: Oxford University Press, 2010, vol. 55.
[32] A. Gupta, \Classification of Complex UCI Datasets Using Machine Learning Algorithms Using Hadoop," International Journal of Scetific & Techology Research, vol. 4, no. 05, pp. 85{94, 2015.
[33] M. Bastian, S. Heymann, and M. Jacomy, \Gephi: An Open Source Software for Exploring and Manipulating Networks," Third International AAAI Conference on Weblogs and Social Media, pp. 361{362, 2009.
[34] D. B. Calçada, S. O. Rezende, and M. S. Teodoro, \Analysis of decomposition parameters of green manure in the Brazilian Northeast with Association Rules Networks," in I International Conference on Agro BigData and Decision Support Systems in Agriculture, Montevideo, Uruguay, 2017, pp. 63{65.
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