Location difference of multiple distances based k-nearest neighbors algorithm
作者：
Highlights：
• The “location difference of multiple distances” and a method LDMDBA are proposed.
• LDMDBA has a time complexity of O(logdnlogn) and does not rely on tree structures.
• Only LDMDBA can be efficiently applied to high dimensional data.
• LDMDBA has a time complexity of (logdlogn) for predicting a new data point.
• LDMDBA has very good stability and can be applied to large databases.
摘要
•The “location difference of multiple distances” and a method LDMDBA are proposed.•LDMDBA has a time complexity of O(logdnlogn) and does not rely on tree structures.•Only LDMDBA can be efficiently applied to high dimensional data.•LDMDBA has a time complexity of (logdlogn) for predicting a new data point.•LDMDBA has very good stability and can be applied to large databases.
论文关键词：Location difference of multiple distances,k-nearest neighbors,Tree structure
论文评审过程：Received 17 March 2015, Revised 22 September 2015, Accepted 25 September 2015, Available online 3 October 2015, Version of Record 8 November 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.09.028
