文章基本信息

标题：CFKM: An Optimal Consensus Clustering Using Fuzzy Based Kernel Mapping Algorithm
本地全文：下载
作者：P.Sathyasri ; K.Jeyalakshmi
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2016
卷号：4
期号：2
页码：1511
DOI：10.15680/IJIRCCE.2016.0402123
出版社：S&S Publications
摘要：Clustering is the application of data mining techniques to discover patterns from the datasets. Thus, when multi-temporal images are considered, they allow us to detect many possible differences in HS images. This paper proposed a novelapproach to measures the data dissimilarity data elements in high dimensional data clustering. Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points.The algorithm called "Fuzzy neighboring consensus clustering based on kernelFuzzificationdegree (FNCKF)", which takes as key measures of correspondence between pairs of data points. The proposed method is to establish a unified framework for on both supervised and unsupervised data sets. Also, we examine some important factors, such as the clustering quality and assortment of basic partitioning, which may affect the performances of Fuzzy framework.Experimental results obtained on synthetic and real datasets to demonstrate the effectiveness of the clustering quality
关键词：Consensus Clustering; nearest neighbours; Fuzzy logic; kernel mapping