Analysis Partition Clustering and Similarity Measure on Al-Quran Verses

A. Huda, M. Deyana, Q. U. Safitri, W. Darmalaksana, Ulfa Rahmani, Mahmud
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引用次数: 2

Abstract

Clustering text is an important application in data mining. This is related by grouping similar text documents together. In this study, several models are builts to classify Qur’anic verses on Surah Al-Baqarah using three clustering technique: kmeans, bisecting kmeans, and k-medoid. Every verse in Surah al-Baqarah represented as a document derived from the translation of the Qur’an in English. Three similarity measures are also used: cosine similarity, jaccard similarity, and correlation coefficient. Then, the cluster of each combination of clustering technique with similarity measure is evaluated using average within cluster distance and davies bouldin index. The result show that the best performance is achieved by using the hemodoidal combined with cosine similarity. Finally obtained the category verses in the Surah al-Baqarah that correlate with each other.
分析《古兰经》经文的分区聚类和相似性度量
文本聚类是数据挖掘中的一项重要应用。这与将相似的文本文档分组有关。本研究使用三种聚类技术:kmeans、bisecting kmeans 和 k-medoid 建立了多个模型,用于对《巴卡拉哈经》中的古兰经文进行分类。巴卡拉经》中的每一节经文都是来自《古兰经》英文译本的文档。此外,还使用了三种相似度量:余弦相似度、jaccard 相似度和相关系数。然后,使用聚类内平均距离和戴维斯-博尔丁指数对每种聚类技术与相似性度量组合的聚类进行评估。结果表明,使用半环形和余弦相似度组合的聚类效果最好。最后获得了《巴卡拉经》中相互关联的经文类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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