NDLI: Kernel Based Fuzzy C-Means Clustering for Chronic Sinusitis Classification

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Kernel Based Fuzzy C-Means Clustering for Chronic Sinusitis Classification

Content Provider	Scilit
Author	Putri, Rezki Aulia Rustam, Zuherman Pandelaki, Jacub
Copyright Year	2019
Description	Journal: Iop Conference Series: Materials Science and Engineering Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both sides of the nose, and behind the eyes. Chronic sinusitis is caused by infection, growth of nasal polyps, or irregularities of the nasal septum. This condition can affect teenagers, adults, and even children. To classify sinusitis we use Kernel Based Fuzzy C-Means (FCM) Clustering Algorithm, which is the development of Fuzzy C-Means (FCM) Algorithm. FCM is one of the widely used clustering technique. FCM algorithm comprises of sample points used to make whole and sub vector spaces according to the size of the distance. However, when non-linear data is separated, the convergence is inaccurate and slow. To overcome this problem, a Kernel-Based Fuzzy C-Means algorithm that makes use of kernel functions as a substitute for Euclidean distance utilized. It maps out samples to high-dimensional space to increase the differences between cluster centres, so they can overcome FCM deficiencies and improve linear machine capabilities. Data was obtained from the laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia, with a 100% accuracy.
Related Links	https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052060/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/546/5/052060
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	5
Volume Number	546
Language	English
Publisher	IOP Publishing
Publisher Date	2019-06-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Artificial Intelligence Kernel Based Fuzzy C Means
Content Type	Text
Resource Type	Article

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