Title :
Correlation-based K-nearest neighbor algorithm
Author :
Li, Xinran ; Xiang, Chenhui
Author_Institution :
Int. Sch., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Data classification is an important task of data mining, and developing high-powered classification algorithm is one of the key procedures for data mining. This paper provides an improved K-nearest neighbor algorithm-correlation-based K-nearest neighbor algorithm. This new algorithm makes data classification base on the correlation calculation, and uses a modified probability to improve the computational speed and prediction accuracy. The experimental results show that the correlation-based K-nearest neighbor algorithm is more suitable to classify massive high-dimensional data sets while comparing with traditional K-nearest neighbor algorithm.
Keywords :
correlation methods; data analysis; data mining; probability; computational speed improvement; correlation calculation; correlation-based k-nearest neighbor algorithm; data classification; data mining; high-dimensional data sets; high-powered classification algorithm; prediction accuracy; probability; Accuracy; Robustness; K-nearest neighbor algorithm; correlation; data classification;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2012 IEEE 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-2007-8
DOI :
10.1109/ICSESS.2012.6269436