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A short analytical presentation of a data-driven study that examines narrator relationships in Sahih al-Bukhari through sequential pattern mining and equivalence-class techniques.
Article
A short analytical presentation of a data-driven study that examines narrator relationships in Sahih al-Bukhari through sequential pattern mining and equivalence-class techniques.
Overview
A short analytical presentation of a data-driven study that examines narrator relationships in Sahih al-Bukhari through sequential pattern mining and equivalence-class techniques.
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This article presents a compact overview of a computational study on chains of transmission in Sahih al-Bukhari. Its aim is to show how contemporary data-mining methods can help reveal sequential patterns among narrators without displacing the classical scholarly concern for isnad analysis.
The study described in the article separates chains from matn material, normalizes narrator names, restructures the data into sequential transactions, and then applies a sequential-pattern-discovery algorithm based on equivalence classes. In this way, it extracts recurring transmission patterns and formulates rules with support, confidence, and lift measures.
Among the reported outcomes are numerous recurrent narrator relationships and thematic links that appear through the chains. The article presents this as a methodological aid that can illuminate transmission structures and open a new technical window onto hadith networks.
Its broader contribution is exploratory: it argues that digital analysis can serve the science of hadith by offering new tools for visualization, comparison, and research support while remaining anchored to the traditional importance of the isnad.
Original publication
This page presents an organized in-site version of the article within the website archive, while the original publication remains available on Alukah Network.