Publication:
Automatic clustering algorithm for interval data based on overlap distance

datacite.subject.fos oecd::Natural sciences::Computer and information sciences
dc.contributor.author Ngoc Lethikim
dc.contributor.author Tuan Lehoang
dc.contributor.author Tai Vovan
dc.date.accessioned 2022-11-09T09:47:04Z
dc.date.available 2022-11-09T09:47:04Z
dc.date.issued 2021
dc.description.abstract In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best advantages of the proposed algorithm is that ACAI figure out simultaneously the appropriate number of groups, and factors in every group. The proposed algorithm can be effectively performed through a Matlab procedure. Based on the extracted intervals from texture of images, we have applied ACAI to recognize the images, an interesting and challenging issue at present. Experimental data sets including the differences of the characteristics as well as the number of elements has shown the reasonableness of the proposed algorithm, and its advantages in comparing to the surviving ones. From the image recognition problem, this research has shown prospect in practical applications for many fields.
dc.identifier.doi 10.1080/03610918.2021.1900248
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/1111
dc.language.iso en_US
dc.relation.ispartof Communications in Statistics - Simulation and Computation
dc.relation.issn 0361-0918
dc.relation.issn 1532-4141
dc.subject automatic
dc.subject cluster analysis
dc.subject CR index
dc.subject interval data
dc.subject overlap distance
dc.title Automatic clustering algorithm for interval data based on overlap distance
dc.type journal-article
dspace.entity.type Publication
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