A metric or distance function is a function which defines a distance between elements of a set. In clustering, measuring the similarity between objects has become an important issue. In practice, there are various similarity measures used and this includes the Euclidean, Manhattan and Minkowski. In this paper, an improved Chebyshev similarity measure is introduced to replace existing metrics (such as Euclidean and standard Chebyshev) in clustering analysis. The proposed measure is later realized in analyzing blood cancer images. Results demonstrate that the proposed measure produces the smallest objective function value and converge at the lowest number of iteration. Hence, it can be concluded that the proposed distance metric contribute in producing better clusters.
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11 December 2015
INNOVATION AND ANALYTICS CONFERENCE AND EXHIBITION (IACE 2015): Proceedings of the 2nd Innovation and Analytics Conference & Exhibition
29 September–1 October 2015
Kedah, Malaysia
Research Article|
December 11 2015
An improved Chebyshev distance metric for clustering medical images
Aseel Mousa;
Aseel Mousa
a)
1School of Computing UUM College of Arts and Sciences,
Universiti Utara Malaysia
06010 Sintok, Kedah, MALAYSIA
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Yuhanis Yusof
Yuhanis Yusof
b)
2School of Computing UUM College of Arts and Sciences,
Universiti Utara Malaysia
06010 Sintok, Kedah, MALAYSIA
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a)
Corresponding author: [email protected]
AIP Conf. Proc. 1691, 040020 (2015)
Citation
Aseel Mousa, Yuhanis Yusof; An improved Chebyshev distance metric for clustering medical images. AIP Conf. Proc. 11 December 2015; 1691 (1): 040020. https://doi.org/10.1063/1.4937070
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