Choice of Cumulative Percentage in Principal Component Analysis for Regionalization of Peninsular Malaysia Based on the Rainfall Amount

Shaharudin, Shazlyn Milleana; Ahmad, Norhaiza

doi:10.1007/978-981-10-6502-6_19

Cited by 6 publications

(9 citation statements)

References 15 publications

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“…Reducing a large dimension dataset to lower dimension while maintaining the original variability in the dataset is the purpose of the PCA [9]. An observation set of feasibly interconnected variables which transforms into a set of linearly uncorrelated ones, namely principal component (PC), helps achieve the above-mentioned purposes.…”

Section: Methodology Principal Component Analysismentioning

confidence: 99%

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A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

Nor¹,

Shaharudin²,

Ismail³

et al. 2021

eer

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In high dimensional data, Principal Component Analysis (PCA)-based Pearson correlation remains broadly employed to reduce the data dimensions and to improve the effectiveness of the clustering partitions. Besides being prone to sensitivity on non-Gaussian distributed data, in a high dimensional data analysis, this algorithm may influence the partitions of cluster as well as generate exceptionally imbalanced clusters due to its assigned equal weight to each observation pairs. To solve the unbalanced clusters in hydrological study caused by skewed character of the dataset, this study came out with a robust method of PCA in term of the correlation. This study will explain a RPCA to be proposed as an alternative to classical PCA in reducing high dimensional dataset to a lower form as well as obtain balance clustering result. This study improved where RPCA managed to downweigh the far-from-center outliers and develop the cluster partitions. The results for both methods are compared in term of number of components and clusters obtained as well as the clustering validity. Regarding the internal and stability validation criteria, this study focuses on the cluster's quality in order to validate the results of clusters obtained for both methods. From the findings, the amount of clusters had improved significantly by using RPCA compared to classical PCA. This proved that the proposed approach are outliers resistant than classical PCA as the proposed approach made a thorough observation assessment and downweigh the ones which were distant from the data center.

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Section: Methodology Principal Component Analysismentioning

confidence: 99%

“…For this research, Tukey's biweight with BP at 0.2, 0.4, 0.6 and 0.8 respectively are evaluated. An experiment by [16] and [9] had shown that for most conditions, the best performance was by a BP of 0.4. Such studies also found out that the results were more precise and effective than others.…”

Section: Pearson Correlation Matrixmentioning

confidence: 99%

A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

Nor¹,

Shaharudin²,

Ismail³

et al. 2021

eer

Self Cite

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“…The use of the PCA helps to recognize patterns by explaining the variance of a large set of inter-correlated variables, and it transforms them into a smaller set of independent factors (PCs) [31]. According to Jollife [42], significant PCs were chosen by the criterion (least 70% of cumulative percentage of total variation) which are clarified by Shaharudin et al [19] and are the best benchmark for cutting off the eigenvalues in a large dataset for extracting the number of components of the analysed variables with reasonable interpretation.…”

Section: Applying Pca To Identify the Dominating Features/components Of Rainfall Clustersmentioning

confidence: 99%

“…Meanwhile, according to Dai [18], they employed the CA approach to the annual rainfall dataset at 49 rain gauges in Somerset, Southwestern England. In India, monthly rainfall data from 1901 to 2002 for 32 rainfall stations, partially arid clusters using k-means clustering, which was located in the individual districts of India with either entirely or partly arid lands experiencing hot and cold weather [19]. According to Halkidi et al [20] and Shaharudin et al [21], CA is one of the most useful tasks in identifying the groups and interesting patterns in the underlying data.…”

Section: Introductionmentioning

confidence: 99%

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Basri

Shaharudin

Kismiantini

et al. 2021

IJGI

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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.

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“…Scree test (Cattell, 1966) which trace the eigenvalues in descending order of their magnitude in relation to their number of factors and determines where they stabilize (D'agostino and Russell, 2005). Percentage of variance explained (Jolliffe, 1972;Shaharudin and Ahmad, 2017); this technique retains components that account for at least of the total variance. Cumulative Percentage of Variance extracted retains components where certain percentages of the cumulative have been suggested;…”

Section: Letmentioning

confidence: 99%

Self-Organizing Maps and Principal Component Analysis to Improve Classification Accuracy

Lazaar¹,

YounessTabii²

2018

RJASET

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The aim of this study is to perform the Kohonen Self-Organizing Map (SOM) using Principal Component Analysis (PCA). SOM is an algorithm commonly used to visualize and classify datasets, due to its ability to project large data into a smaller dimension. However, their performance decreases when the size of the problem becomes too big. Therefore, reducing the size of the data by removing irrelevant or redundant variables and selecting only the most significant ones according to certain criteria has become a requirement before any classification, this reduction should give the best performance according to a certain objective function. Many researchers have tried to solve this problem. This study presents a new approach to improve SOM based on PCA. The experimental analysis of real data from the UCI machine learning repository shows an improvement of the proposed SOM compared to a traditional approach. More than 2% of the improvement in the accuracy of the classification is observed.

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Choice of Cumulative Percentage in Principal Component Analysis for Regionalization of Peninsular Malaysia Based on the Rainfall Amount

Cited by 6 publications

References 15 publications

A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Self-Organizing Maps and Principal Component Analysis to Improve Classification Accuracy

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