Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5615
Title: Clustering uncertain interval data using a new Hausdorff-based metric
Authors: Zarandi, Mohammad Hossein Fazel
Avazbeigi, Milad
Anssari, M. H.
Türkşen, İsmail Burhan
Keywords: Clustering interval data
Component
Hausdorff distance
Pattern recognition
Uncertain interval data
Issue Date: 2010
Source: 2010 Annual North American Fuzzy Information Processing Society Conference, NAFIPS'2010, 12 July 2010 through 14 July 2010, Toronto, ON, 81691
Abstract: This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to validate the method. Results show that the method can identify appropriate clusters efficiently. © 2010 IEEE.
URI: https://doi.org/10.1109/NAFIPS.2010.5548291
https://hdl.handle.net/20.500.11851/5615
ISBN: 9781424478576
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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