Effectiveness measurement of spectral clustering algorithm
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Published
Dec 11, 2017
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Abstract
Abstract
After the Kuwil method was found for applying the spectral clustering algorithm, we need a way to make sure of the results, because in many cases the nature of data is not compatible with the algorithm; also, when the data contain more than three dimensions (3D) the results cannot be displayed on the monitor. So I found two techniques, first, for measuring the strength and effectiveness of S.C.A, such as some comparative relationships that measure the following: Effectiveness of algorithm applying, the strength of every cluster and the effectiveness of data correlation inside every cluster. Secondly, analysis of variance (ANOVA) for S.C.A; this depends on distance variance instead of values variance. I applied the methods above to calculate the strength and effectiveness of S.C.A, and they showed good results, so they can offer more reliability for the outputs of the algorithm. Using these relations and ANOVA for S.C.A help us to measure the data receptivity for applying the algorithm by ‘Kuwil method’, so the outputs will be more reliable and that will help to spread the use of this algorithm among researchers, analysts and other users.
Keywords: Spectral clustring, algorithm effectiveness, Kuwil method.
After the Kuwil method was found for applying the spectral clustering algorithm, we need a way to make sure of the results, because in many cases the nature of data is not compatible with the algorithm; also, when the data contain more than three dimensions (3D) the results cannot be displayed on the monitor. So I found two techniques, first, for measuring the strength and effectiveness of S.C.A, such as some comparative relationships that measure the following: Effectiveness of algorithm applying, the strength of every cluster and the effectiveness of data correlation inside every cluster. Secondly, analysis of variance (ANOVA) for S.C.A; this depends on distance variance instead of values variance. I applied the methods above to calculate the strength and effectiveness of S.C.A, and they showed good results, so they can offer more reliability for the outputs of the algorithm. Using these relations and ANOVA for S.C.A help us to measure the data receptivity for applying the algorithm by ‘Kuwil method’, so the outputs will be more reliable and that will help to spread the use of this algorithm among researchers, analysts and other users.
Keywords: Spectral clustring, algorithm effectiveness, Kuwil method.
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How to Cite
KUWIL, Farag Homed Ali.
Effectiveness measurement of spectral clustering algorithm.
Global Journal of Computer Sciences: Theory and Research, [S.l.], v. 7, n. 3, p. 112-122, dec. 2017.
ISSN 2301-2587.
Available at: <http://sproc.org/ojs/index.php/gjcs/article/view/2790>. Date accessed: 18 dec. 2017.
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