Data mining by density estimation using wavelet

D. Datta
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Abstract

In the field of radiation protection and nuclear science safety is one of the most important matter. The aim of the safety measure is to mitigate the consequences of accident during mal operation of any nuclear facility. In order to have an appropriate safety measure the mining of the data based on which the models are executed for computing the consequences in terms of the radiation dose is very important. With a view to recent progress in information processing there has been significant development in data mining technology using wavelet methods. However, available survey tells that there is no such application in the said domain on this topic. The goal of this is paper to fill the void. First, the paper presents a high-level data-mining framework that reduces the overall process into smaller components. Then a brief review of applications of wavelets for each component is presented. The paper concludes by discussing the impact of wavelets on data mining research and outlining its potential research directions in the field of nuclear science.
基于小波密度估计的数据挖掘
在辐射防护和核科学领域中,安全是最重要的问题之一。安全措施的目的是减轻任何核设施在非正常运行期间发生事故的后果。为了采取适当的安全措施,为计算辐射剂量的后果而执行模型所依据的数据的挖掘是非常重要的。随着近年来信息处理领域的发展,利用小波方法的数据挖掘技术有了很大的发展。然而,据调查,目前在该领域还没有这方面的应用。本文的目标是填补这一空白。首先,本文提出了一个高级数据挖掘框架,将整个过程简化为更小的组件。然后简要回顾了小波在各个分量中的应用。最后讨论了小波对数据挖掘研究的影响,并概述了其在核科学领域的潜在研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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