Analysis Partition Clustering and Similarity Measure on Al-Quran Verses

Huda, Arief Fatchul; Deyana, Moch. R.; Safitri, Q. U.; Darmalaksana, Wahyudin; Rahmani, Ulfa; Mahmud, Md.

doi:10.1109/icwt47785.2019.8978215

Cited by 5 publications

(18 citation statements)

References 7 publications

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“…The finding and discussion of this study can be summarized as follows:  Depends on [1] and [28], k=7 for k-means to cluster chapter Al-Baqarah.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…There are many papers related to this article. The clustering experiment of [1] utilizes a mixture of k-means clustering techniques, k-medoid, and bisecting k-means, together with Jaccard similarity, correlation coefficients, and cosine similarity produce different validity values. The ideal cluster results, however, in chapter al -Baqarah clustering process given by cosine similarity of k-medoid.…”

Section: Related Workmentioning

confidence: 99%

“…The value of k needs to be determined before implementation, which should be more than one. Based on the experiments of Choiruddin [28] and Huda [1], the optimal value of k to cluster the Al-Baqarah chapter using k-means is seven. Chapter Al-Baqarah has 53 subjects, although some still have the same subject as others.…”

Section: ) Cluster Algorithm Parametersmentioning

confidence: 99%

“…Such themes are not written sequentially but depend on asbabunnuzul ayat (verses) while revealed. Hence, grouping verses of similar characteristics of a text they compose will form a cluster that could reflect any theme in Surah (chapter) Al-Baqarah [1].…”

Section: Introductionmentioning

confidence: 99%

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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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“…The finding and discussion of this study can be summarized as follows:  Depends on [1] and [28], k=7 for k-means to cluster chapter Al-Baqarah.…”

Section: B Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: ) Cluster Algorithm Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ahmed¹,

Baharin²,

Nohuddin³

2020

IJACSA

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show abstract

“…The clustering experiment of [4] employs a mixture of k-means clustering variations, bisecting k-means and k-medoid, in addition to Jaccard and cosine similarity and correlation coefficients, to provide a variety of validation results. However, in the chapter Al-Baqarah clustering process experiments, the best cluster consists of 286 verses formed using cosine similarity with k-medoid.…”

Section: Related Workmentioning

confidence: 99%

K-means variations analysis for translation of English Tafseer Al-Quran text

Ahmed

Baharin

Nohuddin

2023

IJECE

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Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.

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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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Analysis Partition Clustering and Similarity Measure on Al-Quran Verses

Cited by 5 publications

References 7 publications

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

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

K-means variations analysis for translation of English Tafseer Al-Quran text

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

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