文章基本信息

标题：Performance Comparison of Data Mining Algorithms: A Case Study on Car Evaluation Dataset
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作者：Jamilu Awwalu ; Anahita Ghazvini ; Azuraliza Abu Bakar 等
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2014
卷号：13
期号：2
页码：78-82
DOI：10.14445/22312803/IJCTT-V13P117
出版社：Seventh Sense Research Group
摘要：Cars are essentially part of our everyday lives. There are different types of cars as produced by different manufacturers; therefore the buyer has a choice to make. The choice buyers or drivers have mostly depends on the price, safety, and how luxurious or spacious the car is. Data mining tasks in terms of classification or prediction are applied in a variety of domains which includes manufacturing and business. But the choice of algorithm can be confusing because some algorithms are argued to have better performance record than others, depending on the associated task and nature of dataset. This study analyzes the performance of three data mining algorithms in terms of speed and accuracy on the car evaluation dataset obtained from the University of California Irvine (UCI) dataset.
关键词：Data Mining; Decision Tree; Neural Network; Naive Bayesian.