Clustering Interval-valued Data Using an Overlapped Interval Divergence
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conference contribution
posted on 2009-12-01, 00:00 authored by Yongli Ren, Y H Liu, Jia Rong, Robert DewAs 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.
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AusDM '09 Proceedings of the Eighth Australasian Data Mining ConferenceVolume
101Pagination
35 - 42Publisher
ACM Digital LibraryLocation
Melbourne, AustraliaStart date
2009-12-01End date
2009-12-04ISSN
1445-1336ISBN-13
9781920682828Publication classification
CN.1 Other journal articleTitle of proceedings
Conferences in Research and Practice in Information Technology SeriesUsage metrics
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