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

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