一种新的粗糙集近似算子快速提取算法在信息系统分类中的应用

2010 International Conference on Computational and Information Sciences Pub Date : 2010-12-17 DOI:10.1109/ICCIS.2010.168

Ping Song, Xu Zhang

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引用次数: 0

摘要

信息系统分类是数据挖掘的重要组成部分，其目的是分析信息系统，从复杂的数据中提取重要信息，预测数据的未来发展趋势。目前，对数据进行分类的方法有很多，如粗糙集理论、决策树、贝叶斯网络、遗传算法等。本文提出的方法以ID3算法为基础，结合粗糙集理论和决策树理论，以条件属性作为决策树的节点，对信息系统中的数据进行分类。此外，在信息系统分类中采用了一种新的快速逼近算子算法，提高了分类效率。

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Application of a New Fast Algorithm for Getting Approximate Operators of Rough Set to Information System Classification

The information system classification is a crucial part of data mining, which aims to analysis the information system, extract important message from complex data, and forecast the future development trend of data. At present, there are many methods to classify the data, for example, Rough Set Theory, Decision Tree, Bayesian Network, Genetic Algorithm, etc. The method presented in this paper, based on ID3 Algorithm, associated with the combination of Rough Set Theory and Decision Tree Theory, uses the conditional attribute as the decision tree's node to classify data in the information system. Moreover, a new fast algorithm for getting approximate operators is used in the information system classification to improve the efficiency.

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2010 International Conference on Computational and Information Sciences

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