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Clustering Interval-valued Data Using an Overlapped Interval Divergence

Version 2 2024-06-04, 02:46
Version 1 2016-10-11, 09:22
conference contribution
posted on 2024-06-04, 02:46 authored by Y Ren, YH Liu, J Rong, Robert DewRobert Dew
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 di®erent adaptive clustering frameworks. The experiment results show that the proposed OLID is more suitable for interval data than the Hausdor® distance and the cityblock distance. © 2009, Australian Computer Society, Inc.

History

Volume

101

Pagination

35-42

Location

Melbourne, Australia

Start date

2009-12-01

End date

2009-12-04

ISSN

1445-1336

ISBN-13

9781920682828

Publication classification

CN.1 Other journal article

Title of proceedings

Conferences in Research and Practice in Information Technology Series

Event

AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference

Publisher

ACM Digital Library

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