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Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Read chapter Read first chapter

2017 | OriginalPaper | Chapter

An Association Rule Mining Approach in Predicting Flood Areas

Authors : Mokhairi Makhtar, Nur Ashikin Harun, Azwa Abd Aziz, Zahrahtul Amani Zakaria, Fadzli Syed Abdullah, Julaily Aida Jusoh

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

This study focuses on the application of Association rules mining for the flood data in Terengganu. Flood is one of the natural disasters that happens every year during the monsoon season and causes damage towards people, infrastructure and the environment. This paper aimed to find the correlation between water level and flood area in developing a model to predict flood. Malaysian Drainage and Irrigation Department supplied the dataset which were the flood area, water level and rainfall data. The association rules mining technique will generate the best rules from the dataset by using Apriori algorithm which had been applied to find the frequent itemsets. Consequently, by using the Apriori algorithm, it generated the 10 best rules with 100% confidence level and 40% minimum support after the candidate generation and pruning technique. The results of this research showed the usability of data mining in this field and can help to give early warning towards potential victims and spare some time in saving lives and properties.

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Literature
1.
Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Mining least association rules of degree level programs selected by students. Int. J. Multimedia Ubiquit. Eng. 9(1), 241–253 (2014)CrossRef
2.
Agrawal, R., Imielinski, T., Swami, A.: Mining association in large databases. In: Proceedings of 1993 ACM SIGMOD International Conference Management Data - SIGMOD 1993, pp. 207–216 (1993)
3.
Athiyaman, B., Sahu, R.: Hybrid data mining algorithm: an application to weather data. J. Indian Res. 1(4), 71–83 (2013)
4.
Aziz, A.A., Harun, N., Makhtar, M., Hassan, F., Jusoh, J.A., Zakaria, Z.A.: A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules. J. Theor. Appl. Inf. Technol. 87(3), 512–519 (2016)
5.
Cortez, P., Morais, A.: A data mining approach to predict forest fires using meteorological data. In: Neves, M.F.S.J., Machado, J. (eds.) New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007, Portuguese Conference on Artificial Intelligence, Guimarães, Portugal, pp. 512–523 (2007)
6.
Cremaschi, P., Carriero, R., Astrologo, S., Coli, C., Lisa, A., Parolo, S., Bione, S.: An association rule mining approach to discover lncRNAs expression patterns in cancer datasets. Biomed Res. Int. 146250 (2015)
7.
Djatna, T., Alitu, I.M.: An application of association rule mining in total productive maintenance strategy: an analysis and modelling in wooden door manufacturing industry. Procedia Manuf. 4, 336–343 (2015)CrossRef
8.
Klemettinen, M., Mannila, H.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of Third International Conference Information Knowledge Management, pp. 401–407 (1994)
9.
Nath, N.D., Meena, M.J., Syed Ibrahim, S.P.: Mining frequent itemsets in real time. In: Vijayakumar, V., Neelanarayanan, V. (eds.) ISBCC – 2016. SIST, vol. 49, pp. 325–334. Springer, Heidelberg (2016)CrossRef
10.
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Ann. Pharmacother. 42(1), 62–70 (2008)
11.
Rameshkumar, K., Sambath, M., Ravi, S.: Relevant association rule mining from medical dataset using new irrelevant rule elimination technique. In: 2013 International Conference on Information Communication and Embedded Systems, ICICES 2013, pp. 300–304 (2013)
12.
Sun, J., Yu, W., Zhao, H.: Study of association rule mining on technical action of ball games. In: 2010 International Conference Meas Technology Mechatronics Automation ICMTMA 2010, vol. 3, pp. 539–542 (2010)
13.
Tanna, P., Ghodasara, Y.: Using Apriori with WEKA for frequent pattern mining. arXiv Preparation arXiv1406.​7371, vol. 12, no. 3, pp. 127–131 (2014)
14.
Vannozzi, G., Croce, U.D., Starita, A., Benvenuti, F., Capozzo, A.: Knowledge discovery in databases of biomechanical variables: application to the sit to stand motor task. J. Neuroeng. Rehabil. 1(7), 1–10 (2004)
15.
Yang, R., Tang, J., Sun, D.: Association rule data mining applications for Atlantic tropical cyclone intensity changes. Weather Forecast. 26(3), 17 (2011)CrossRef
16.
Zheng, L., Shen, C., Tang, L., Zeng, C., Li, T., Luis, S., Chen, S.C.: Data mining meets the needs of disaster information management. IEEE Trans. Hum.-Mach. Syst. 43(5), 451–464 (2013)CrossRef
Metadata
Title
An Association Rule Mining Approach in Predicting Flood Areas
Authors
Mokhairi Makhtar
Nur Ashikin Harun
Azwa Abd Aziz
Zahrahtul Amani Zakaria
Fadzli Syed Abdullah
Julaily Aida Jusoh
Copyright Year
2017
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_44

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