A case-based reasoning approach for pattern detection in Malaysia rainfall data Online publication date: Fri, 02-Oct-2015
by Almahdi Mohammed Alshareef; Azuraliza Abu Bakar; Abdul Razak Hamdan; Sharifah Mastura Syed Abdullah; Mohammed Alweshah
International Journal of Big Data Intelligence (IJBDI), Vol. 2, No. 4, 2015
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.
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