基于邻域粗糙集的混合数据聚类算法及其应用

Akarsh Goyal, Rahul Chowdhury
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引用次数: 0

摘要

近年来,人们开发了许多聚类算法,其主要功能是使对象集具有几乎相同的特征。但是由于分类数据值的存在,这些算法在实现上面临着挑战。此外,一些能够处理分类数据的算法无法处理值中的不确定性,因此存在稳定性问题。因此,由于这些困难,必须处理带有不确定性的分类数据。为此,2007年提出了一种基于基本粗糙集理论的MMR算法。MMeR于2009年提出,在处理分类数据方面超过了MMR的结果,但不能鲁棒地用于混合数据。本文将带邻域关系的MMeR算法进行了推广,并将其建立为邻域粗糙集模型,本文称之为最小平均邻域粗糙度(Min Mean neighborhood Roughness, MMeNR)。它负责异构数据。此外,作者还扩展了MMeNR方法,使其适用于地理空间数据分析和流行病学等各种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application
In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.
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