【作者】 汤琳；

【导师】 陶宏才；

【作者基本信息】 西南交通大学 ， 计算机应用技术， 2007， 硕士

【摘要】 数据挖掘是近年来兴起的一个新的研究领域，它综合了数据库技术、人工智能、统计学等多个学科，最终目的是从大量的数据资料中发现有价值的信息和知识，即蕴涵在数据中的信息，从而达到为决策支持的目的。分类规则挖掘则是通过对训练样本数据集的学习构造分类规则的过程，是数据挖掘、知识发现的一个重要方面，其实质是希望得到高准确性、易于理解的和有趣的分类规则。遗传算法是一种基于生物进化论和分子遗传学的全局随机搜索算法。本文对基于遗传算法的分类规则挖掘进行了研究，并在此基础上，提出了基于相似度的交叉变异算子，改进了种间竞争遗传算法，并将它们应用到分类规则的挖掘中。论文首先回顾了数据挖掘的历史背景，并对数据挖掘的基本概念、过程、特点、分类及任务模式进行了详细地归纳和总结，着重讨论了分类规则挖掘的步骤、技术和方法。此后，对遗传算法的生物由来、特点和理论基础进行了概述，总结了简单遗传算法的流程，三大算子和四大关键问题，并分析和讨论了简单遗传算法应用于分类规则挖掘中存在的“早熟”收敛现象发生的原因和一般的解决方法。接着，在简单遗传算法基础上，为了克服简单遗传算法“早熟”收敛的问题，引入“相似度”和“杂交优势”思想，将原来的交叉和变异两个算子合二为一，提出了一个新的遗传算子：基于相似度的交叉变异算子，并利用Breast cancer data数据集对其进行了算法测试。最后，改进了种间竞争算法，并利用adult数据集对其进行了算法测试。更多还原

【Abstract】 Data Mining is a new research realm rising in recent years, and synthesizes many fields of science such as the database technique, artificial intelligence and statistics etc. Its main job is to discover valuable information and knowledge hidden behind data from numerous data in order for decision support. Mining classification rules is a procedure to construct a classifier through studying training dataset, and is a very important part of Data Mining and Knowledge Discovery. In essence, its goal is to discover classification rules which are highly accurate, comprehensible and interesting. Genetic Algorithm (GA) is an overall random searching method based on the theory of evolution and molecular genetics.This thesis has done some research on classification based on Genetic Algorithm, proposes a new operator called similarity-based crossover-mutation, improves a genetic algorithm based on the competition between populations, and applies the new operator and the improved algorithm to mine the classification rules.In this thesis, we look back the history background of data mining at first, and sum up the basic concepts, process, characteristics, classification and the mission modes of data mining in detail, emphasizing to discuss the steps, techniques and methods for classification rule mining. Then, this thesis introduces the biologic origin, the characteristics and the theories foundations of GA, sums up the process of the Simple Genetic Algorithm (SGA), three operators and four key problems. Also, it analyzes and discusses the reason and resolution of the premature phenomena when SGA is applied in mining classification rules. Furthermore, in order to overcome the premature phenomena, based on the SGA, this thesis introduces the idea of "similarity degree" and "beneficial crossover", and puts forwards a new operator called similarity-based crossover-mutation by combining crossover with mutation together, and applies the new operator into mining classification rules on Breast cancer data. Finally, this thesis improves a genetic algorithm based on the competition between populations, and tests the improved algorithm by adult data.更多还原