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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Evaluation of Subspace Clustering Using Internal Validity Measures

OSZUST, M. See more information about OSZUST, M. on SCOPUS See more information about OSZUST, M. on IEEExplore See more information about OSZUST, M. on Web of Science, KOSTKA, M. See more information about KOSTKA, M. on SCOPUS See more information about KOSTKA, M. on SCOPUS See more information about KOSTKA, M. on Web of Science
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Download PDF pdficon (1,280 KB) | Citation | Downloads: 530 | Views: 2,413

Author keywords
pattern recognition, data mining, subspace clustering, clustering validation, distance metrics

References keywords
clustering(19), data(13), information(9), subspace(8), algorithms(7), measures(6), machine(6), evaluation(6), systems(5), review(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 141 - 146
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03020
Web of Science Accession Number: 000360171500020
SCOPUS ID: 84940728824

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Different clustering algorithms, or even the same algorithm with different input parameters, can produce different data partitioning. Then, clustering validity measures are applied in order to determine which results have better quality than others. External measures can be used for evaluation of clustering algorithms on datasets with known data division. However, in a real scenario such information is not available, and here internal measures are often applied. Subspace clustering techniques can create clusters which utilise different subsets of the full feature space. From this reason, a calculation of internal measures using the full feature space distance metrics (e.g., Euclidean distance) is not justified. In this paper, we propose a novel approach to subspace clustering evaluation with internal quality measures, i.e., we apply distance metrics that are able to handle missing attribute values or are used in dimensionality reduction techniques. Our approach is verified on eight publicly available, widely-used datasets. Obtained results are promising and allow recommending proposed distance metrics to be suitable for calculation of examined internal validation measures.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 7,121 TCR
SCOPUS® Citations for all references: 9,488 TCR

Web of Science® Average Citations per reference: 223 ACR
SCOPUS® Average Citations per reference: 297 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-05-20 10:53 in 141 seconds.

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