Title :
Clustering uncertain interval data using a new Hausdorff-based metric
Author :
Zarandi, M. H Fazel ; Avazbeigi, M. ; Anssari, M.H. ; Turksen, I.B.
Author_Institution :
Dept. of Ind. Eng., Amirkabir Univ. of Technol. (AUT), Tehran, Iran
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.
Keywords :
data analysis; pattern clustering; uncertainty handling; Hausdorff based metric; interval distance measurement; uncertain interval data clustering; Clustering algorithms; Clustering methods; Data analysis; Extraterrestrial measurements; Industrial engineering; Iterative algorithms; Paper technology; Partitioning algorithms; Pattern recognition; Space exploration; Clustering Interval Data; Hausdorff Distance; Pattern Recognition; Uncertain Interval Data; component;
Conference_Titel :
Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American
Conference_Location :
Toronto, ON
Print_ISBN :
978-1-4244-7859-0
Electronic_ISBN :
978-1-4244-7857-6
DOI :
10.1109/NAFIPS.2010.5548291