Hierarchical Cluster Approach for Regionalization of Peninsular Malaysia based on the Precipitation Amount

Ahmad, Normah; Othman, Ida Rosmini; Deni, Sayang Mohd

doi:10.1088/1742-6596/423/1/012018

Cited by 19 publications

(17 citation statements)

References 4 publications

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“…Generally, cluster analysis techniques are exploratory data analysis tools whose purpose is to solve classification problems. This means sorting data relative to something under study (events, people, objects…) into groups, called clusters, in such a way that these groups can achieve maximum internal homogeneity (within the cluster) and maximum external heterogeneity (between clusters) (Salah et al 2012;Ahmad et al 2013).…”

Section: Multivariate Clustering Analysismentioning

confidence: 99%

Poor housing and stroke mortality in population over 64 years old at the parish level, in mainland Portugal

et al. 2016

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Housing conditions can impact physical and mental health. In 2013, Portugal was still the fourth European Union country with the highest percentage of population without an adequately heated home in winter. Other adverse conditions are, for instance, overcrowding and living in older buildings. Some studies stress the relationship between stroke and poor living conditions in the elderly population, especially cold homes. Univariate and multivariate spatial cluster analysis were used to explore the relationship between excess stroke death risk, from 1998 to 2004, measured by the standardized mortality ratio (SMR) for several cohorts (including persons over 64 years old), and poor housing variables from the 2001 census, at the parish level in continental Portugal. A multivariate cluster of parishes, with population without any form of heating their homes as dominant condition, was detected in northwest Portugal. Mean and median SMR values across all cohorts were consistently higher within this cluster. This strengthens the hypothesis that cold homes deserve more attention in stroke prevention and mitigation amongst elderly persons, especially in northwestern continental Portugal.

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Section: Multivariate Clustering Analysismentioning

confidence: 99%

Poor housing and stroke mortality in population over 64 years old at the parish level, in mainland Portugal

et al. 2016

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“…In New Zealand, Mosley (1981) used multivariate cluster analysis to define the hydrological regions. Modarres (2006) and Ahmad et al (2013) respectively identified the homogeneous precipitation regions of Iran and Malaysia using a hierarchical clustering approach. Sahin and Cigizoglu (2010), Goyal et al 2019, andBasalirwa (1995) respectively in Turkey, India, and Uganda also applied clustering approaches for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

et al. 2021

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The knowledge of the climate pattern for a particular region is important to alleviate the impact of climate change and protect the environment by taking appropriate actions based on geographical knowledge. It is also equally important for water resources planning and management. In this study, the regional disparities and similarities have been revealed among different climate stations or regions in Bangladesh based on different climatological factors such as rainfall, temperatures, relative humidity, sea level pressure, cloud cover, wind speed, the sunshine hour. We have selected one of the best-fitted algorithms for particular climate data from three multivariate clustering approaches named hierarchical clustering, partitioning around medoids (PAM), and K-means clustering by using different validation tests. Four homogeneity tests (Mann-Kendall Test, Pettitt's test, Buishand Range Test, Standard Normal Homogeneity Test) also have been performed for each of the clusters created based on several factors. The results suggest that the climate regions or meteorological stations of Bangladesh can be clustered into two groups based on a combination of climatological variables.According to the findings, there is a huge variation between the two groups in terms of climatological factors. The first group (cluster 1) is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second group (cluster 2) contains rain-prone and hilly regions, which are mostly situated in the southern part. All newly defined clusters show homogeneous behavior with few exceptions such as clusters based on sea level pressure are not homogeneous.

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“…Some of the most popular methods in identifying the rainfall patterns through data mining approaches are Principal Component Analysis (PCA) and cluster analysis. These approaches have been well-known for many years and applied in a wide range of research fields such as classification of weather types, climate regionalization and circulation patterns associated to climate extremes [1][2][3]. PCA reduces the dimension of the data matrix which is commonly employed as a pre-processing method for the benefit of guiding the classification process.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification

Shaharudin

Ismail

Nor

et al. 2019

IJ-AI

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<p>In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and bi-clustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCA-kMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.</p>

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Hierarchical Cluster Approach for Regionalization of Peninsular Malaysia based on the Precipitation Amount

Cited by 19 publications

References 4 publications

Poor housing and stroke mortality in population over 64 years old at the parish level, in mainland Portugal

Poor housing and stroke mortality in population over 64 years old at the parish level, in mainland Portugal

Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification

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