基于蚁群优化在Web数据挖掘分类模型的实现  

Research on the Web Data Mining Model of Classification Based on ACO

作　　者：吴林旭[1] 姚跃华[1] 黄晶[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,湖南长沙410076

出　　处：《计算机工程与科学》2009年第3期89-91,共3页Computer Engineering & Science

摘　　要：蚁群优化是人工智能领域中群体智能的分支之一,已经成功地应用于旅行推销员、作业调度选择等优化问题上,但用它解决数据挖掘问题还是一个新的研究课题。本文提出一种蚂蚁分类算法Ant_Miner3,并在Web数据挖掘中采用相应的页面优化分类方法,对非结构化数据集的处理进行了相关的研究和优化。经实验验证,该算法能够导出更优更简洁的分类规则。Ant colony optimization （ACO） is a branch of a newly developed form of artificial intelligence called swarm intelligence. It has been applied successfully to the traveling salesman problem, job scheduling, router choice and other combinatorial optimization problems. But it is still a new research topic in data mining. A novel classification algorithm named Ant_Miner3 based on ant behaviors to solve the classification problem in data mining tasks is exploited to the Web page classification of Web data mining, and the processing of the non-structural dataset is carried out. The experimental verification shows the algorithm can derive more concise and better classification rules.

关 键 词：蚁群算法 WEB挖掘 数据挖掘 分类规则 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]