基于双重遗传的聚类分析算法研究

针对影响k-means聚类效果的聚类数目和初始中心点两大因素，提出了基于双重遗传的k-means算法。它用外层遗传算法控制聚类数目，用内层遗传算法控制聚类的初始中心点，并采用类间距离和类内距离以及二者之间的比值来评价聚类结果的好坏，在算法终止后，可同时求得较优的聚类数目和某聚类数目下的较优初始中心点。此外，根据内外层遗传算法的特殊性，采用不同的编码策略适应算法需求，为保留优质个体，采用精英个体保留策略。通过UCI数据集测试实例证明此算法有很好的实用性，对数据挖掘技术有一定参考价值。...

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Bibliographic Details
Published in计算机工程与科学 Vol. 39; no. 12; pp. 2320 - 2325
Main Author 文静;曹妍;张琳;牟向伟
Format Journal Article
LanguageChinese
Published 大连海事大学交通运输管理学院,辽宁大连,116026 2017
Subjects
k-means算法
分层编码
双重遗传
精英保留
聚类分析
elitism preservation
k-means算法
double genetic
分层编码
精英保留
双重遗传
cluster analysis
聚类分析
k-means algorithm
layered coding
Online AccessGet full text
ISSN1007-130X
DOI10.3969/j.issn.1007-130X.2017.12.022

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Summary:针对影响k-means聚类效果的聚类数目和初始中心点两大因素，提出了基于双重遗传的k-means算法。它用外层遗传算法控制聚类数目，用内层遗传算法控制聚类的初始中心点，并采用类间距离和类内距离以及二者之间的比值来评价聚类结果的好坏，在算法终止后，可同时求得较优的聚类数目和某聚类数目下的较优初始中心点。此外，根据内外层遗传算法的特殊性，采用不同的编码策略适应算法需求，为保留优质个体，采用精英个体保留策略。通过UCI数据集测试实例证明此算法有很好的实用性，对数据挖掘技术有一定参考价值。
Bibliography:WEN Jing,CAO Yan,ZHANG Lin,MU Xiang-wei （College of Transportation Management,Dalian Maritime University,Dalian 116026 ,China）
43-1258/TP
There are two major factors that affect the k-means clustering effect： the number of clustering and the initial choice of the centroids. We put forward an improved k-means algorithm based on the double genetic algorithm, which uses the outer sub-genetic algorithm to control the number of clus- tering, and the inner sub-genetic algorithm to control the initial choice of cluster centroids, and utilizes the intra-class distance and inter-lass distance as well as the ratio between them to evaluate the clustering results. We therefore can get both the optimal number of clustering and the corresponding optimal initial cluster centroids by this improved k-means method. In addition, given the specificity of the inner and outer sub-generic algorithms, the improved k-means algorithm uses two different encoding strategies, and in order to preserve excellent individuals, it also uses the
ISSN:1007-130X
DOI:10.3969/j.issn.1007-130X.2017.12.022