Providing an Ontology-Based Method for Exploring the Association Rules in Multi-Agent Distributed Environments

Document Type : Original Article

Authors

1 ihu

2 iust.ac.ir

Abstract

Distributed association rules mining is one of the most important data mining methods that extracts the inter dependence of data items from decentralized data sources, regardless of their physical location and is based on the process of extracting repeated items. When exploration algorithms are implemented on    large-scale data, a large number of recurring items are produced, many of which are irrelevant,            ambiguous, and unusable for the business, thus causing a challenge called "combination explosion ". In this paper, a new coalition method based on distributed data mining and domain archeology, abbreviated to DARMASO, is proposed to address this challenge. This method uses three algorithms: the                    DARMASOMAIN algorithm to guide and control the process of exploration and aggregation of universal rules, the DARMASOPRU algorithm to reduce and prune the data and the DARMASOINT algorithm to explore and aggregate the rules of all the generated data sources. DARMASO uses a map-reduce-based distributed computational model in a multi-agent distributed environment. It also provides a practical way for semantic mining of large-scale data sets. This method filters out the association rules of generality based on the purposes of data mining as well as the needs of the user and only produces and maintains useful rules. Reducing the scope of exploration and filtration of rules is achieved through the process of semantic pruning in the form of removing inappropriate candidates from the set of frequent items and    producing association rules of utility. The implementation is performed using a data set from the scope of natural disasters and the earthquake class. It also improves the speed and quality of rule extraction and generates practical, reliable, logical, quality and valuable rules to support decision-making amid the  masses of data.
 

Keywords

References

[1]     D. Patel and J. Shah, “Jade Agent Framework for Distributed Data Mining and Pattern Analysis,” International Journal of Computer Applications, vol. 975, p. 8887, 2017.##
[2]             H. M. Safhi, B. Frikh, and B. Ouhbi, “Assessing reliability of Big Data Knowledge Discovery process,” Procedia computer science, vol. 148, pp. 30-36, 2019.##
[3]             R. M. Gahar, O. Arfaoui, M. S. Hidri, and N. B.             Hadj-Alouane, “An Ontology-driven MapReduce Framework for Association Rules Mining in Massive Data,” Procedia Computer Science, vol. 126, pp. 224-233, 2018.##
[4]     B. Eine, M. Jurisch, and W. Quint, “Ontology-based big data management,” Systems, vol. 5, no. 3, p. 45, 2017.##
[5]     P. V. Bhagat, P. M. J. I. J. O. R. Gourshettiwar, I. T. i. Computing, and Communication, “A survey paper on ontology-based approaches for semantic data mining,” vol. 3, no. 4, pp. 2137-2141, 2015.##
[6]             M. R. Chikhale, “Study of Distributed Data Mining Algorithm and Trends,” IOSR Journal of Computer Engineering (IOSR-JCE), pp. 41-47, 2016.##
[7]     D. Dou, H. Wang, and H. Liu, “Semantic data mining: A survey of ontology-based approaches,” In Proceedings of the 2015 IEEE 9th international conference on semantic computing (IEEE ICSC 2015), IEEE, pp. 244-251, 2015.##
[8]     V. S. Ms and K. J. P. C. S. Shah, “Performance evaluation of distributed association rule mining algorithms,” vol. 79, pp. 127-134, 2016.##
[9]     R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.##
[10]  Z. Farzanyar, “Development of Algorithms for Detecting Frequent Items Set to Large-Scale Peer-to-Peer Environments with Flow Data Attitudes,” Iran University of Science and Technology, 1391. (In Persian)##
[11]          D. Apiletti, E. Baralis, T. Cerquitelli, P. Garza, F. Pulvirenti, and L. Venturini, “Frequent itemsets mining for big data: a comparative analysis,” Big data research, vol. 9, pp. 67-83, 2017.##
[12]          D. Xia, X. Lu, H. Li, W. Wang, Y. Li, and Z. Zhang, “A MapReduce-based parallel frequent pattern growth algorithm for spatiotemporal association analysis of mobile trajectory big data,” Complexity, 2018.##
[13]          D. C. Anastasiu, J. Iverson, S. Smith, and G. Karypis, “Big data frequent pattern mining,” In Frequent pattern mining: Springer, pp. 225-259, 2014.##
[14]  A. G. Touzi, H. B. Massoud, and A. Ayadi, “Automatic ontology generation for data mining using fca and clustering,” arXiv preprint arXiv:1311.1764, 2013.##
[15]  D. A. Koutsomitropoulos and A. K. Kalou, “A standards-based ontology and support for Big Data Analytics in the insurance industry,” ICT Express, vol. 3, no. 2, pp. 57-61, 2017.##
[16]          A. Soylu et al., “Ontology-based Visual Querying with OptiqueVQS: Statoil and Siemens Cases,” 2016.##
[17]          S. Urmela and M. Nandhini, “Approaches and Techniques of Distributed Data Mining: A Comprehensive Study,” International Journal of Engineering and Technology (IJET), 2017.##
[18]  S. Patil, S. Karnik, and V. Sawant, “A Review on          Multi-Agent Data Mining Systems,” International Journal of Computer Science and Information Technologies, vol. 6, no. 6, pp. 4888-4893, 2015.##
[19]  F. Jiang, “Efficient frequent pattern mining from big data and its applications,” Ph.D. Thesis, Department of Computer ScienceThe University of Manitoba Winnipeg, Manitoba, Canada, 2014.##
[20]   Y. Lin, P.-C. Huang, D. Liu, and L. Liang, “Scalable frequent-pattern mining on nonvolatile memories,” In 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, pp. 578-583, 2017.##
[21]  Bhamra, Gurpreet S Verma, Anil K Patel, Ram B, “Agent Based Frameworks for Distributed Association Rule Mining: An Analysis,” International Journal in Foundations of Computer Science & Technology (IJFCST), 2015.##
[22]   H. Saberi, M. R. Kangavari, and M. R. H. Ahangar, “Provide an agent-oriented architecture for semantic exploration of large-scale data in distributed environments,” Scientific-Research Journal of Electronic and Cyber Defense, 1398. [Online]. Available: https://ecdj.ihu.ac.ir, In Persian.##
[23]  H. M. Basir, H. Saberi, and M. A. Javadzadeh, “Provide a method for selecting data based on the ontology of the organization's goals,” presented at the Fifth National Conference on Defense Science and Engineering, Tehran, 1398. (In Persian)##
[24]  A. H. Yazdi, “Extract association rules from semantic data streams,” Computer Engineering, Ferdowsi University of Mashhad, 1392. [Online]. Available: http://www.um.ac.ir,  In Persian##
 
Electronic and Cyber Defense
Volume 9, Issue 1 - Serial Number 33
Serial No. 33, Spring Quarterly
April 2021
Pages 1-17

Files

History

  • Receive Date: 17 February 2020
  • Revise Date: 04 June 2020
  • Accept Date: 05 August 2020
  • Publish Date: 21 April 2021

Share

How to cite

Statistics