Paper #3

Text Clustering of Tafseer Translations by Using k-means Algorithm: An Al-Baqarah Chapter View

Mohammed A. Ahmed, Hanif Baharin and Puteri NE. Nohuddin

Abstract: Al-Quran is Muslims’ main book of belief and behaviour. The Al-Quran is used as a reference book by millions of Muslims worldwide, and as such, it is useful for Muslims in general and Muslim academics to gain knowledge from it. Many translators have worked on the Quran’s translation into many different languages around the world, including English. Thus, every translator has his/her own perspectives, statements, and opinions when translating verses acquired from the (Tafseer) of the Quran. However, this work aims to cluster these variations among translations of the Tafseer by utilising text clustering. As a part of the text mining approach, text clustering includes clustering documents according to how similar they are. This study adapted the (k-means) clustering technique algorithm (unsupervised learning) to illustrate and discover the relationships between keywords called features or concepts for five different translators on the 286 verses of the Al-Baqarah chapter. The datasets have been preprocessed, and features extracted by applying TF-IDF (Term Frequency-Inverse Document Frequency). The findings show two/three-dimensional clustering plotting for the first two/three most frequent features assigned to seven cluster categories (k=7) for each of five translated Tafseer. The features ‘allah/god’, ‘believ’, and ‘said’ are the three most features shared by the five Tafseer.

Keywords: Al-Baqarah chapter; k-means algorithm; Text clustering; Text mining; Tafseer translation.