Int. J. of Big Data Intelligence   »   2015 Vol.2, No.4

 

 

Title: A case-based reasoning approach for pattern detection in Malaysia rainfall data

 

Authors: Almahdi Mohammed Alshareef; Azuraliza Abu Bakar; Abdul Razak Hamdan; Sharifah Mastura Syed Abdullah; Mohammed Alweshah

 

Addresses:
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia
Institute of Climate Change, University Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia
Department of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan

 

Abstract: A pattern detection algorithm for a rainfall time series dataset is proposed. However, the adaptive sliding window algorithm (ASWA) and the improved case-based reasoning (CBR) approach namely: the data segmentation phase of pattern detection adapts the classical sliding window algorithm (SWA) to compute change points in time series rainfall data, where the ASWA improves the SWA with respect to two parameters, namely, error and window size. In pattern detection phase, the goal is to detect the class labels of the rainfall dataset. Two phases of CBR are improved: the retrieval phase is adapted to compute rainfall sequences with unequal window sizes and the revise phase is improving classification accuracy. Experiments show that the proposed ASWA generates a smaller number of windows compared to the classical SWA while the proposed improved CBR approach detects the patterns of segmented data from which experts can determine the class labels for the patterns.

 

Keywords: data mining; CBR; case-based reasoning; big data; pattern detection; rainfall data; data streams; data segmentation; classification; pattern discovery; Malaysia; precipitation data; rainfall patterns; adaptive sliding window algorithm; ASWA.

 

DOI: 10.1504/IJBDI.2015.072172

 

Int. J. of Big Data Intelligence, 2015 Vol.2, No.4, pp.285 - 302

 

Available online: 02 Oct 2015

 

 

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