A NEW FUZZY K-NEAREST NEIGHBORS ALGORITHM

被引：0

作者：

Li, Chengjie ^{[1
]}

Pei, Zheng ^{[1
]}

Li, Bo ^{[1
]}

Zhang, Zhen ^{[1
]}

机构：

[1] Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China

来源：

INTELLIGENT DECISION MAKING SYSTEMS, VOL. 2 | 2010年

关键词：

K-nearest searching algorithm; KNNA; ISODATA; F-KNNA; ID3;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

The K-nearest searching algorithm (KNNA) is widely used in the high dimension space. However, it has two shortcomings, one is expensive searching cost, the other is the accuracy of the result is not very high In this paper, a new algorithm (F-KNNA) is proposed the methods and steps are as follows. 1) classify the training set using fuzzy ISODATA; 2) make a hyper-cone with membership of search object, which connects the origin point and the search object The experiment shows that the performance of F-KNNA is superior to the regular KNNA

引用

页码：246 / +

页数：2

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