Paper Info
Title
Interval-valued Data Clustering based on the Range City Block metric
Abstract
This paper introduces a new approach to Data Clustering on interval-valued data. Nowadays dissimilarity measures for interval-valued data uses representative point distance. It was defined the Range City Block metric. Interval-valued input distance matrix is used to process hierarchical clustering by single linkage with partial ordering. The new method based on the Range City Block Metric can allows for a better analysis of the clustering results on interval-valued data.
Year
DOI
Venue
2016
10.1109/SMC.2016.7844246
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Keywords
Field
DocType
interval-valued data analysis,partial ordering,single linkage,hierarchical clustering,interval-valued input distance matrix,representative point distance,dissimilarity measures,range city block metric,interval-valued data clustering
k-medians clustering,Hierarchical clustering,Fuzzy clustering,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,Cluster analysis,Machine learning
Conference
ISSN
ISBN
Citations 
1062-922X
978-1-5090-1898-7
2
PageRank 
References 
Authors
0.43
5
1
Name
Order
Citations
PageRank
Sergio Mario Lins Galdino130.78