Location difference of multiple distances based k-nearest neighbors algorithm
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- 作者：Shuyin Xiaa ; syxia@cqu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; 380835019@qq.com" class="auth_mail" title="E-mail the corresponding author ; Zhongyang Xionga ; zyxiong@cqu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Yueguo Luoa ; c ; 362011523@qq.com" class="auth_mail" title="E-mail the corresponding author ; Limei Dongb ; 37638721@qq.com" class="auth_mail" title="E-mail the corresponding author ; Guanghua Zhanga ; guanghua0307@qq.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Location difference of multiple distances ; k-nearest neighbors ; Tree structure
- 刊名：Knowledge-Based Systems
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：90
- 期：Complete
- 页码：99-110
- 全文大小：914 K
文摘
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The “location difference of multiple distances” and a method LDMDBA are proposed.
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LDMDBA has a time complexity of O(logdnlogn) and does not rely on tree structures.
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Only LDMDBA can be efficiently applied to high dimensional data.
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LDMDBA has a time complexity of (logdlogn) for predicting a new data point.
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LDMDBA has very good stability and can be applied to large databases.