Mousa, Aseel and Yusof, Yuhanis (2015) An improved Chebyshev distance metric for clustering medical images. In: 2nd Innovation and Analytics Conference & Exhibition (IACE 2015), 29 September –1 October 2015, TH Hotel, Alor Setar, Kedah, Malaysia.

Full text not available from this repository. (Request a copy)

Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Published in AIP Conference Proceedings, Volume 1691, Issue 1, ISBN: 978-0-7354-1338-2
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	18 Jan 2017 03:48
Last Modified:	18 Jan 2017 03:48
URI:	https://repo.uum.edu.my/id/eprint/20648

Actions (login required)

View Item