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

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


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "Big Data - " before the paper title in OpenConf.

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Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  3/2015 - 20

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: 182 | Views: 770

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

Abstract
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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

[1] S.-H. Liao, P.-H. Chu, and P.-Y. Hsiao, "Data mining techniques and applications - A decade review from 2000 to 2011," Expert Systems with Applications, vol. 39, no.12, pp. 11303-11311, 2012.
[CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 180]


[2] R. Xu and D. C. Wunsch II, Clustering, New York, NY, USA, Wiley/IEEE Press, 2009

[3] R. Xu and D. C. Wunsch II, "Clustering algorithms in biomedical research: a review," Biomedical Engineering, IEEE Reviews, vol. 3, pp. 120-154, 2010.
[CrossRef] [SCOPUS Times Cited 84]


[4] A. Nagpal, A. Jatain, and D. Gaur, "Review based on data clustering algorithms," Information & Communication Technologies (ICT), 2013 IEEE Conference on., pp. 298-303, April 2013.
[CrossRef] [SCOPUS Times Cited 10]


[5] C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications, CRC Press, 2013.

[6] A. Patrikainen and M. Meila, "Comparing subspace clusterings," IEEE Transactions on Knowledge and Data Engineering, vol. 18:7, pp. 902-916, 2006.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 58]


[7] H. P. Kriegel, P. Kroger, and A. Zimek, "Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 3:1, no. 1, 2009.
[CrossRef] [Web of Science Times Cited 255] [SCOPUS Times Cited 435]


[8] B. S. S. M. zu Eissen and F. Wisbrock, "On cluster validity and the information need of users," in Proc. 3rd Int. Conference on Artificial Intelligence and Applications (AIA 03), 2003.

[9] L. Parsons, E. Haque, and H. Liu, "Subspace clustering for high dimensional data: a review," ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 90-105, 2004.
[CrossRef]


[10] S. Günnemann, I. Färber, E. Müller, I. Assent, and T. Seidl, "External evaluation measures for subspace clustering," in Proceedings of the 20th ACM international conference on Information and knowledge management, ACM, pp. 1363-1372, 2011.
[CrossRef] [SCOPUS Times Cited 36]


[11] S. Ben-David and M. Ackerman, "Measures of clustering quality: A working set of axioms for clustering," in Proceedings of the Advances in Neural Information Processing Systems, pp. 121-128. 2008.

[12] N. X. Vinh, J. Epps, and J. Bailey, "Information theoretic measures for clusterings comparison: is a correction for chance necessary?," in Proceedings of the 26th Annual International Conference on Machine Learning, ACM, pp. 1073-1080, 2009.
[CrossRef] [SCOPUS Times Cited 2]


[13] N. X. Vinh, J. Epps, and J. Bailey, "Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance," Journal of Machine Learning Research, vol. 11, pp. 2837-2854, 2010.

[14] E. Muller, S. Gunnemann, I. Assent, and T. Seidl, "Evaluating clustering in subspace projections of high dimensional data," in Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 1270-128, 2009.
[CrossRef]


[15] E. Bae and J. Bailey, "Enriched spatial comparison of clusterings through discovery of deviating subspaces," Machine Learning, vol. 98, no. 1-2, pp. 93-120, 2015.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[16] M. Hassani, Y. Kim, S. Choi, and T. Seidl, "Subspace clustering of data streams: new algorithms and effective evaluation measures," Journal of Intelligent Information Systems, Springer US, pp. 1-17, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 3]


[17] U. Markowska-Kaczmar and A. Hurej, "Evaluation of subspace clustering quality," Hybrid Artificial Intelligence Systems, Springer Berlin Heidelberg, pp. 400-407, 2008.
[CrossRef] [SCOPUS Times Cited 1]


[18] D. L. Davies and D. W. Bouldin, "A cluster separation measure," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 2, pp. 224-227, 1979.
[CrossRef] [SCOPUS Times Cited 1847]


[19] C. L. Blake and C. J. Merz, "UCI Repository of machine learning databases http://archive.ics.uci.edu/ml/ ," Irvine, CA: University of California. Department of Information and Computer Science, 1998.

[20] S. Gajawada, and D. Toshniwal, "Vinayaka: a semi-supervised projected clustering method using differential evolution," International Journal of Software Engineering and Applications (IJSEA), vol. 3, no. 4, pp. 77-85, 2012.
[CrossRef]


[21] P. Garcia-Laencina, J. Sancho-Gomez, and A. Figueiras-Vidal, "Pattern classification with missing data: a review," Neural Comput. Appl., vol. 19 no. 2, pp. 263-282. 2010.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 105]


[22] C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park, "Fast algorithms for projected clustering," in ACM SIGMoD Record, vol. 28, no. 2, pp. 61-72, ACM,1999.
[CrossRef]


[23] U. Maulik and S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no.12, pp.1650-1654, 2002.
[CrossRef] [Web of Science Times Cited 422] [SCOPUS Times Cited 542]


[24] P. J. Rousseeuw, "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis," Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.

[25] O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J.-M. Pérez, and I. Perona, "An extensive comparative study of cluster validity indices," Pattern Recognition, vol. 46, no. 1, pp. 243-256, 2013.
[CrossRef] [Web of Science Times Cited 97] [SCOPUS Times Cited 131]


[26] G. E. A. P. A. Batista and M. C. Monard, "Experimental comparison of k-nearest neighbour and mean or mode imputation methods with the internal strategies used by C4.5 and CN2 to treat missing data," University of Sao Paulo, 2003.

[27] E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, "Dimensionality reduction for fast similarity search in large time series databases," Knowledge and information Systems, vol. 3, no. 3, pp. 263-286, 2001.
[CrossRef]


[28] E. Achtert, H.-P. Kriegel, and A. Zimek, "ELKI: a software system for evaluation of subspace clustering algorithms," in Proceedings of the 20th international conference on Scientific and Statistical Database Management, SSDBM '08, pp. 580-585. Springer Berlin / Heidelberg, 2008.
[CrossRef] [SCOPUS Times Cited 12]


[29] A. Hein and T. Kirste, "Unsupervised detection of motion primitives in very high dimensional sensor data," in Proceedings of the 5th Workshop on Behaviour Monitoring and Interpretation, BMI'10, Karlsruhe, Germany, 2010.

[30] D. Ingaramo, D. Pinto, P. Rosso, and M. Errecalde, "Evaluation of internal validity measures in short-text corpora," in Computational Linguistics and Intelligent Text Processing, Springer Berlin Heidelberg, pp. 555-567, 2008.
[CrossRef] [SCOPUS Times Cited 19]


[31] J. Handl, J. Knowles, and D.-B. Kell, "Computational cluster validation in post-genomic data analysis," Bioinformatics, vol. 21, no. 15, pp. 3201-3212, 2005.
[CrossRef] [Web of Science Times Cited 359] [SCOPUS Times Cited 405]




References Weight

Web of Science® Citations for all references: 1,362 TCR
SCOPUS® Citations for all references: 3,871 TCR

Web of Science® Average Citations per reference: 43 ACR
SCOPUS® Average Citations per reference: 121 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 2017-04-19 08:53 in 147 seconds.




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