Analysis of The Theme Clustering Algorithm Using K-Means Method

Erwin Dwika Putra, M. H. Rifqo, Dwita Deslianti, Krismiyani Krismiyani
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引用次数: 1

Abstract

The title of this research is the analysis of the thesis theme clustering algorithm using the k-means method. The main problem is how we can find out which theme is most in demand by thesis students at the Faculty of Engineering, University of Muhammadiyah Bengkulu. This clustering uses the K-means method. The K-Means method was chosen because this method is one of the non-hierarchical data clustering methods that seeks to partition data into two or more clusters with the same characteristics included in the same cluster. The purpose of this research is to help prospective students who will write their thesis in knowing which themes are more interested in them.
基于k -均值方法的主题聚类算法分析
本研究的题目是利用k-means方法对论文主题聚类算法进行分析。主要的问题是我们如何找出哪个主题是最需要的论文学生在工程学院,穆罕默迪亚大学Bengkulu。这种聚类使用K-means方法。选择K-Means方法是因为该方法是一种非分层数据聚类方法,它试图将数据划分为两个或多个具有相同特征的聚类。这项研究的目的是帮助未来的学生谁将写他们的论文知道哪些主题更感兴趣。
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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