Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

Eka Haryati, Annisa and Surono, Sugiyarto and Tanu Wijaya, Toomy and Khang Wen, Goh and Thobirin, Aris (2022) Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering. International Journal Of Artificial Intelegence Research, 6 (1). ISSN 2579-7298

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Abstract

The fuzzy clustering algorithm is a partition method that assigns
objects from a data set to a cluster by marking the average location.
Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming
distance and exponential membership function is used to analyze the
cluster center of a data point. The data point with the highest density
will be the cluster's center. Therefore, this research aims to determine
the number of collections with the best quality by comparing the
Partition Coefficient (PC) values for each number produced. The data
set, which is heart failure patient data, is 150 data obtained from UCI
Machine Learning. The data consists of 11 variables, including age
(

Item Type:	Artikel Umum
Subjects:	Q Science > Q Science (General)
Divisi / Prodi:	Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika)
Depositing User:	Dr Sugiyarto Surono
Date Deposited:	03 Aug 2022 03:49
Last Modified:	03 Aug 2022 03:49
URI:	http://eprints.uad.ac.id/id/eprint/36096

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