A Modified K-Nearest Neighbor Algorithm to Handle Uncertain Data

被引：0

作者：

Agrawal, Rashmi ^{[1
]}

Ram, Babu ^{[2
]}

机构：

[1] Manav Rachna Int Univ, Faulty Engn & Technol, Faridabad, India

[2] Manav Rachna Int Univ, Fac Comp Applicat, Faridabad, India

来源：

2015 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND SECURITY (ICITCS) | 2015年

关键词：

classification; k-nearest neighbor; uncertainty; uncertain data; distance metric;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Classification is an important technique in data mining. The K-Nearest neighbor (K-NN) algorithm is a memory based algorithm and is capable of producing satisfactory results when applied on certain data but the distance measures used in this algorithm is not capable of handling the data sets containing the uncertain attribute values. Data uncertainty is common in real word applications. In this paper we have proposed an effective distance measure and modified K-NN which can be applied on the data sets containing uncertain numerical attributes and gives satisfactory results.

引用

页数：4

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