Comparison of distance measurement on k-nearest neighbour in textual data classification

W. Wahyono, I. N. P. Trisna, Sarah Lintang Sariwening, M. Fajar, Danur Wijayanto
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引用次数: 8

Abstract

One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian or Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.
文本数据分类中k近邻距离度量的比较
在文档自动组织应用中,文本数据分类的一种算法是KNN算法,它将单词表示形式转化为向量。在测量数据元素之间的紧密度时,KNN算法中的距离计算至关重要。本研究比较了KNN中常用的四种距离计算方法,即欧几里得、切比雪夫、曼哈顿和闵可夫斯基。该数据集使用了Youtube上Eminem的评论数据,其中包含448个数据。本研究表明,与Chebycev和Manhattan算法相比,Euclidian或Minkowski在KNN算法上取得了最好的结果。当K值为3时,在KNN上得到最好的结果。
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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6 weeks
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