NDLI: Analysis of medical datasets by using geodesic based approximate spectral clustering

Content Provider	IEEE Xplore Digital Library
Author	Yalcin, B. Tasdemir, K. Yildirim, I.
Copyright Year	2015
Description	Author affiliation: Bilgisayar Muhendisligi Bolumu, Uluslararasi Antalya Univ., Turkey (Tasdemir, K.) \|\| Elektron. ve Haberlesme Muhendisligi Bolumu, Istanbul Teknik Univ., Istanbul, Turkey (Yalcin, B.; Yildirim, I.)
Abstract	Fast and accurate analysis of medical data is of great importance for diagnosis and treatment. In line with the technological developments, the size and diversity of these data have been increasing, which in turn makes their assessment difficult. Therefore, there is an ever growing need for automated decision support systems. To this end, clustering based applications are rapidly increasing in medicine thanks to their limited user interaction, no requirement of labeled training samples, and their effectiveness for decision and estimation. Among clustering methods, approximate spectral clustering (ASC), which depends on a pairwise similarity matrix of data points, has been recently popular and successful thanks to its ability to find irregularly shaped clusters and its independence from parametric cluster models. Various similarity criteria have been proposed to represent pairwise similarities. In this study, medical datasets have been analyzed by approximate spectral clustering with our geodesic distance based similarity criteria. Experimental results indicate that our approach performs better than traditional k-means and Euclidean based approximate spectral clustering.
Starting Page	1
Ending Page	4
File Size	322256
Page Count	4
File Format	PDF
e-ISBN	9781467377652
DOI	10.1109/TIPTEKNO.2015.7374585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-15
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Reactive power Geodesic similarity criteria Medical data analysis Magnetic resonance imaging MRI Approximate spectral clustering Benchmark testing Gadolinium Pattern recognition Medical diagnostic imaging
Content Type	Text
Resource Type	Article

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