The main source of various religious teachings is their sacred texts which
vary from religion to religion based on different factors like the geographical
location or time of the birth of a particular religion. Despite these
differences, there could be similarities between the sacred texts based on what
lessons it teaches to its followers. This paper attempts to find the similarity
using text mining techniques. The corpus consisting of Asian (Tao Te Ching,
Buddhism, Yogasutra, Upanishad) and non-Asian (four Bible texts) is used to
explore findings of similarity measures like Euclidean, Manhattan, Jaccard and
Cosine on raw Document Term Frequency [DTM], normalized DTM which reveals
similarity based on word usage. The performance of Supervised learning
algorithms like K-Nearest Neighbor [KNN], Support Vector Machine [SVM] and
Random Forest is measured based on its accuracy to predict correct scared text
for any given chapter in the corpus. The K-means clustering visualizations on
Euclidean distances of raw DTM reveals that there exists a pattern of
similarity among these sacred texts with Upanishads and Tao Te Ching is the
most similar text in the corpus.

本文试图通过文本挖掘技术，探索亚洲和非亚洲的宗教经典之间的相似性，并使用监督学习算法，测量其正确预测各种经文的准确性。利用欧几里得、曼哈顿、Jaccard 和 Cosine 等相似性度量方法以及基于文档单词出现频率和规范化文档单词出现频率等相似性度量方法，得到了一些发现。K-means 聚类可视化揭示了宗教经典之间的相似性模式，其中乌梅尼西亚和道德经是语料库中最相似的文本。