| 数据挖掘中分类方法综述 |
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| 引用本文： | 钱晓东.数据挖掘中分类方法综述[J].图书情报工作,2007,51(3):68-71. |
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| 作者姓名： | 钱晓东 |
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| 作者单位： | 天津市天津大学电气与自动化工程学院 |
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| 摘 要： | 数据挖掘中的核心技术分类算法的内容及其研究现状进行综述。认为分类
算法大体可分为传统分类算法和基于软计算的分类法两类，主要包括相似函数、关联规
则分类算法、K近邻分类算法、决策树分类算法、贝叶斯分类算法和基于模糊逻辑、遗传
算法、粗糙集和神经网络的分类算法。通过论述以上算法优缺点和应用范围，研究者对
已有算法的改进有所了解，以便在应用中选择相应的分类算法。
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| 关 键 词： | 数据挖掘 分类 软计算 |
| 收稿时间： | 2006-03-27 |
| 修稿时间： | 2006-03-252006-07-23 |
A Review on Classification Algorithms in Data Mining |
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| Qian Xiaodong.A Review on Classification Algorithms in Data Mining[J].Library and Information Service,2007,51(3):68-71. |
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| Authors: | Qian Xiaodong |
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| Institution: | School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 |
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| Abstract: | As one of the kernel techniques in the data mining, it is necessary to summarize the research status of classification algorithm. Classification algorithms can be divided into classical algorithms and algorithms based on soft computing, primarily including similar function, classification algorithms based on association rule, K-nearest Neighbor, decision tree, Bayes network and classification algorithms based on fuzzy logic, genetic algorithm, neural network and rough sets. By presenting the advantages and disadvantages and the application range of the algorithms mentioned above, it will be helpful for people to improve and select algorithms for applications, and even to develop new ones. |
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| Keywords: | data mining classification soft computing |
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