基于SAW的K-Means聚类决策支持系统:确定论文主题

Erna Daniati, Arie Nugroho
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引用次数: 10

摘要

毕业论文是学生毕业的基本要求。学生根据自己的兴趣选择论文题目。事实上,很多学生的论文选题不合适,导致他们的论文质量很差。解决这一问题的途径之一是以决策支持系统(DSS)的形式开发信息系统。决策支持系统需要数据建模和处理来生成备选决策。数据建模采用K-Means聚类的形式。这个过程为每个主题生成聚类和权重。采用简单加性加权法,利用权重生成备选决策。结合K-Means和SAW可以快速生成计算以产生备选决策。这个解决方案,以支持选题除了有助于选择论文题目根据学生的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
K-Means clustering with Decision Support System using SAW: Determining thesis topic
Thesis is an essential requirement for students to graduate. Students choose thesis topic according to their interests. In fact, many students choose inappropriate topic for their thesis and cause their thesis quality are bad. One of ways to solve the problem is to develop information system in form of Decision Support System (DSS). DSS needs data modeling and process to generate alternative decisions. Data modeling is in form of clustering using K-Means. This process generates clusters and weights to each topic. Weight is used to generate alternative decisions using Simple Additive Weighting Method. Combination K-Means and SAW can generate calculation fast to produce alternative decisions. This solution to support topic selection is excepted to contribute choosing thesis topic according to students ability.
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