聚类的遗传算法:初步研究

R. Krovi
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引用次数: 64

摘要

聚类分析是一种用于发现数据中的模式和关联的技术。更具体地说,它是一个多元统计过程,从包含某些变量信息的数据集开始,并试图将这些数据案例重新组织成相对均匀的组。研究人员在聚类分析方面遇到的主要问题之一是,不同的聚类方法可以并且确实对同一数据集产生不同的解。我们需要的是一种能够发现数据集中最“自然”组的技术。遗传算法属于一类“人工智能”技术,它建立在自然选择和自然遗传学的原则之上。这项研究的主要目标是研究使用遗传算法进行聚类的潜在可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic algorithms for clustering: a preliminary investigation
Cluster analysis is a technique which is used to discover patterns and associations within data. More specifically, it is a multivariate statistical procedure that starts with a data set containing information on some variables and attempts to reorganize these data cases into relatively homogeneous groups. One of the major problems encountered by researchers, with regard to cluster analysis that different clustering methods can and do generate different solutions for the same data set. What is needed, is a technique that has discovered the most 'natural' groups in a data set. Genetic algorithms belong to a class of 'artificially intelligent' techniques, that are founded on principles of natural selection and natural genetics. The primary goal of this research effort is to investigate the potential feasibility of using genetic algorithms for the purpose of clustering.<>
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