摘要
决策离不开知识,从数据库中采掘知识是解决从大信息量中获取有用知识的有效途径。本文对当前的一些常用的数据挖掘方法,如神经网络、决策树、K-means聚类算法,粗集和模糊集理论等方法的研究现状进行了评述和总结。在此基础上提出了一种基于内容的数据挖掘算法,它是两阶段检索技术,再加上粗集,近似匹配(模糊集理论),K-means聚类等数据挖掘方法以及用户反馈机制等相结合的算法。最后本文还指出了数据挖掘的未来发展前景。
Decision-making can not be separated from knowledge,extracting knowledge from the database is the effective way to gain the useful knowledge from the large amount of information.This paper has reviewed and summarized the current number of commonly used data mining methods,such as neural networks,decision trees,K-means clustering algorithm,rough sets and fuzzy set theory and other methods of research.On this basis,a content-based data mining algorithms,it is a two-stage retrieval technology,coupled with rough sets,approximate matching(fuzzy set theory),K-means clustering methods,as well as user feedback mechanisms combining algorithms.Finally,the author also predicted the data mining prospects for further development.
出处
《长沙医学院学报》
2009年第1期49-52,共4页
Journal of Changsha Medical University
