An Enhanced K-Nearest Neighbor Algorithm Using Information Gain and Clustering

被引：33

作者：

Taneja, Shweta ^{[1
]}

Gupta, Charu ^{[1
]}

Goyal, Kratika ^{[1
]}

Gureja, Dharna ^{[1
]}

机构：

[1] Guru Gobind Singh Indraprastha Univ, CSE Dept, Bhagwan Parshuram Inst Technol, New Delhi, India

来源：

2014 FOURTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION TECHNOLOGIES (ACCT 2014) | 2014年

关键词：

KNN; Dynamic KNN (DKNN); Distance-Weighted KNN (DWKNN); Weight Adjusted KNN; Information Gain;

D O I：

10.1109/ACCT.2014.22

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

KNN (k-nearest neighbor) is an extensively used classification algorithm owing to its simplicity, ease of implementation and effectiveness. It is one of the top ten data mining algorithms, has been widely applied in various fields. KNN has few shortcomings affecting its accuracy of classification. It has large memory requirements as well as high time complexity. Several techniques have been proposed to improve these shortcomings in literature. In this paper, we have first reviewed some improvements made in KNN algorithm. Then, we have proposed our novel improved algorithm. It is a combination of dynamic selected, attribute weighted and distance weighted techniques. We have experimentally tested our proposed algorithm in NetBeans IDE, using a standard UCI dataset-Iris. The accuracy of our algorithm is improved with a blend of classification and clustering techniques. Experimental results have proved that our proposed algorithm performs better than conventional KNN algorithm.

引用

页码：325 / 329

页数：5

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