Clustering interval-valued data using an overlapped interval divergence

Authors:
Yongli Ren;Yu-Hsn Liu;Jia Rong;Robert Dew
Affiliations:
Zhengzhou University, Zhengzhou, China;Deakin University, Vic, Australia;Deakin University, Vic, Australia;Deakin University, Vic, Australia
Venue:
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Year:
2009

Citing 4
Cited 0

Clustering of interval data based on city-block distances

Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data

Pattern Recognition Letters
Partitional clustering algorithms for symbolic interval data based on single adaptive distances

Pattern Recognition
Dependency bagging

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

As a common problem in data clustering applications, how to identify a suitable proximity measure between data instances is still an open problem. Especially when interval-valued data is becoming more and more popular, it is expected to have a suitable distance for intervals. Existing distance measures only consider the lower and upper bounds of intervals, but overlook the overlapped area between intervals. In this paper, we introduce a novel proximity measure for intervals, called Overlapped Interval Divergence (OLID), which extends the existing distances by considering the relationship between intervals and their overlapped "area". Furthermore, the proposed OLID measure is also incorporated into different adaptive clustering frameworks. The experiment results show that the proposed OLID is more suitable for interval data than the Hausdorff distance and the city-block distance.