An Association Rule Mining Approach in Predicting Flood Areas

Makhtar, Mokhairi; Harun, Nur Ashikin; Aziz, Azwa Abd; Zakaria, Zalmiyah; Abdullah, Fadzli Syed; Jusoh, Julaily Aida

doi:10.1007/978-3-319-51281-5_44

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The approximation of how often a certain event will take place is called frequency analysis (Hosking and Wallis, 1997). There are many statistical methods used in frequency analysis and estimation of the parameters of statistical distribution to predict the probability of upcoming events based on installation the previous observations on selected statistical distributions (Harun et al, 2017;Makhtar et al, 2016;Zhou et al, 2017;Sharafi et al, 2021;Murshed et al 2018). Some methods that rely on moments that have been used over a long period in frequency analysis are not always satisfactory especially when the sample is small.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimate for three-parameter kappa distribution using LHmoments approach

al.¹

2022

Int. j. adv. appl. sci.

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The method of higher-order L-moments (LH-moment) was proposed as a more robust alternative compared to classical L-moments to characterize extreme events. The new derivation will be done for Mielke-Johnson’s Kappa and Three-Parameters Kappa Type-II (K3D-II) distributions based on the LH-moments approach. The data of maximum monthly rainfall for Embong station in Terengganu were used as a case study. The analyses were conducted using the classical L-moments method with η=0 and LH-moments methods with η=1, η=2, η=3 and η=4 for a complete data series and upper parts of the distributions. The most suitable distributions were determined based on the Mean Absolute Deviation Index (MADI), Mean Square Deviation Index (MSDI), and Correlation (r). Also, L-moment and LH-moment ratio diagrams were used to represent visual proofs of the results. The analysis showed that LH-moments methods at a higher order of K3D-II distribution best fit the data of maximum monthly rainfalls for the Embong station for the upper parts of the distribution compared to L-moments. The results also proved that whenever η increases, LH-moments reflect more and more characteristics of the upper part of the distribution. This seems to suggest that LH-moments estimates for the upper part of the distribution events are superior to L-moments in fitting the data of maximum monthly rainfalls.

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Section: Introductionmentioning

confidence: 99%

Parameter estimate for three-parameter kappa distribution using LHmoments approach

al.¹

2022

Int. j. adv. appl. sci.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Moayad Aloqaily . and mobile data streams [12], [13], natural catastrophes forecasting [14], medical [15]- [19], predicting weather [20], and securing networks [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

et al. 2020

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Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.INDEX TERMS Big data applications, pattern recognition, association rules, frequent item set mining, IoT.

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“…At present, the exceptional power of mining and furnishing deep insights of data has made association rule mining a necessary tool. It is currently used in a number of fields such as analyzing market basket data [2], IoT services and infrastructure [3]- [5], smart home [6]- [8], smart retail [9], mining of data streams [10], mining of mobile data stream (mdsm) [11], recommender systems [12], medical [13]- [17], predicting natural catastrophes [18], safeguarding Internet and web [19]- [22], and predicting weather [23].…”

Section: Introductionmentioning

confidence: 99%

TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

et al. 2019

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Sparseness is often witnessed in big data emanating from a variety of sources, including IoT, pervasive computing, and behavioral data. Frequent itemset mining is the first and foremost step of association rule mining, which is a distinguished unsupervised machine learning problem. However, techniques for frequent itemset mining are least explored for sparse real-world data, showing somewhat comparable performance. On the contrary, the methods are adequately validated for dense data and stand apart from each other in terms of performance. Hence, there arises an immense need for evaluating these techniques as well as proposing new ones for large sparse real-world datasets. In this study, a novel method: Mining Frequent Itemsets by Iterative TRimmed Transaction lattICE (TRICE) is proposed. TRICE iteratively generates combinations of varying-sized trimmed subsets of I , where I denote the set of distinct items in a database. Extensive experiments are conducted to assess TRICE against HARPP, FP-Growth, optimized SaM, and optimized RElim algorithms. The experimental results show that TRICE outperforms all these algorithms both in terms of running time and memory consumption. TRICE maintains a substantial performance gap for all sparse real-world datasets on all minimum support thresholds. Moreover, assessment of memory use of optimized SaM and RElim algorithms has been performed for the first time.INDEX TERMS Association rules, big data applications, data mining, frequent itemset mining, pattern recognition, pervasive computing.

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An Association Rule Mining Approach in Predicting Flood Areas

Cited by 4 publications

References 12 publications

Parameter estimate for three-parameter kappa distribution using LHmoments approach

Parameter estimate for three-parameter kappa distribution using LHmoments approach

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

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