Genetic clustering for automatic evolution of clusters and application to image classification
Genetic clustering for automatic evolution of clusters and application to image classification
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DOI：
10.1016/s0031-3203(01)00108-x
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发表时间：
2002-06-01
影响因子：
8
通讯作者：
Maulik, U
中科院分区：
文献类型：
--
作者：
Bandyopadhyay, S;Maulik, U
本文利用遗传算法的搜索能力对任意数据集进行自动进化聚类数量和适当聚类。一个新的字符串表示，包括实数和不关心符号，用于编码可变数量的簇。davis - bouldin指数被用来衡量聚类的有效性。遗传聚类方案的有效性证明了人工和现实数据集。遗传聚类技术的效用也演示了部分城市加尔各答的卫星图像。该方法能够区分出图像中一些特征的地表覆盖类型。(C) 2002模式识别学会。Elsevier Science Ltd.出版。版权所有。
In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as Proper Clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies-Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.