TY - GEN
T1 - A new distance metric for unsupervised learning of categorical data
AU - Jia, Hong
AU - CHEUNG, Yiu Ming
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - Distance metric is the basis of many learning algorithms and its effectiveness usually has significant influence on the learning results. Generally, measuring distance for numerical data is a tractable task, but for categorical data sets, it could be a nontrivial problem. This paper therefore presents a new distance metric for categorical data based on the characteristics of categorical values. Specifically, the distance between two values from one attribute measured by this metric is determined by both of the frequency probabilities of these two values and the values of other attributes which have high interdependency with the calculated one. Promising experimental results on different real data sets have shown the effectiveness of proposed distance metric.
AB - Distance metric is the basis of many learning algorithms and its effectiveness usually has significant influence on the learning results. Generally, measuring distance for numerical data is a tractable task, but for categorical data sets, it could be a nontrivial problem. This paper therefore presents a new distance metric for categorical data based on the characteristics of categorical values. Specifically, the distance between two values from one attribute measured by this metric is determined by both of the frequency probabilities of these two values and the values of other attributes which have high interdependency with the calculated one. Promising experimental results on different real data sets have shown the effectiveness of proposed distance metric.
UR - http://www.scopus.com/inward/record.url?scp=84908474055&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889890
DO - 10.1109/IJCNN.2014.6889890
M3 - Conference proceeding
AN - SCOPUS:84908474055
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1893
EP - 1899
BT - Proceedings of the International Joint Conference on Neural Networks
PB - IEEE
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -