Frequent pattern extraction in the Tafseer of Al-Quran

Chua, Stephanie; Nohuddin, Puteri N. E.

doi:10.1109/ict4m.2014.7020667

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…http:/tanzil.net/trans/ is a portal that offers several translation documents to various Tafseer translators in different languages, including English. Several papers use datasets obtained from this website, such as (Chua & Nohuddin, 2017;Chua & Nohuddin, 2014;Husin et al, 2017;Slamet et al, 2016;Sukmana et al, 2016;Zeroual & Lakhouaja, 2016). The purpose of this analysis is to discover the relationships and to cluster the characteristics of the chapter Al-Baqarah English Tafseer document by (Muhammad Sarwar) translator comprising 286 verses.…”

Section: Methodsmentioning

confidence: 99%

Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Ahmed

Baharin

Nohuddin

2021

JQSR

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Al-Quran is the primary text of Muslims’ religion and practise. Millions of Muslims around the world use al-Quran as their reference guide, and so knowledge can be obtained from it by Muslims and Islamic scholars in general. Al-Quran has been reinterpreted to various languages in the world, for example, English and has been written by several translators. Each translator has ideas, comments and statements to translate the verses from which he has obtained (Tafseer). Therefore, this paper tries to cluster the translation of the Tafseer using text clustering. Text clustering is the text mining method that needs to be clustered in the same section of related documents. The study adapted (mini-batch k-means and k-means) algorithms of clustering techniques to explain and to define the link between keywords known as features or concepts for Al-Baqarah chapter of 286 verses. For this dataset, data preprocessing and extraction of features using Term Frequency-Inverse Document Frequency (TF-IDF) and Principal Component Analysis (PCA) applied. Results showed that two/three-dimensional clustering plotting assigning seven cluster categories (k = 7) for the Tafseer. The implementation time of the mini-batch k-means algorithm (0.05485s) outperformed the time of the k-means algorithm (0.23334s). Finally, the features ‘god’, ‘people’, and ‘believe’ was the most frequent features.

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Section: Methodsmentioning

confidence: 99%

Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Ahmed

Baharin

Nohuddin

2021

JQSR

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“…Most of the articles adapted one method to cluster the text translated Al-Quran (Tafseer) as the dataset for its experiments, such as [5] [7][8][9][10]. This paper has adapted two clustering methods (partitioning and density-based) and compared and analyzed Al-Baqarah's chapter text of English Tafseer as the experiment dataset.…”

Section: Introductionmentioning

confidence: 99%

Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

Ahmed¹,

Baharin²,

Nohuddin³

2020

IJACSA

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Chapter Al-Baqarah is the longest chapter in the Holy Quran, and it covers various topics. Al-Quran is the primary text of Islamic faith and practice. Millions of Muslims worldwide use Al-Quran as their reference book, and it, therefore, helps Muslims and Islamic scholars as guidance of the law life. Text clustering (unsupervised learning) is a process of separation that has to be divided text into the same section of similar documents. There are many text clustering algorithms and techniques used to make clusters, such as partitioning and density-based methods. In this paper, k-means preferred as a partitioning method and DBSCAN, OPTICS as a density-based method. This study aims to investigate and find which algorithm produced as the best accurate performance cluster for Al-Baqarah's English Tafseer chapter. Data preprocessing and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) have applied for the dataset. The result shows k-means outperformed even has the smallest of Silhouette Coefficient (SC) score compared to others due to less implementation time with no noise production for seven clusters of Al-Baqarah chapter. OPTICS has no noise with the medium of SC score but has the longest implementation time due to its complexity.

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“…It aims to extract the interesting relationship, frequent patterns and coloration existing in the set of items in the data repository [8]. ARM has been applied in many real world problems such as finding patterns in documents [9][10], predicting floods [11], trend analysis of social networks [12], monitoring elderly people [13], etc. Various research has been done to see the suitability of data mining in predicting the climate.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Climate Variability in Malaysia Using Association Rule Mining

Rashid¹,

Nohuddin²,

Zainol³

2018

IJET

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Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

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Frequent pattern extraction in the Tafseer of Al-Quran

Cited by 7 publications

References 6 publications

Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

Analyzing Climate Variability in Malaysia Using Association Rule Mining

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