Zagazig University Digital Repository
Home
Thesis & Publications
All Contents
Publications
Thesis
Graduation Projects
Research Area
Research Area Reports
Search by Research Area
Universities Thesis
ACADEMIC Links
ACADEMIC RESEARCH
Zagazig University Authors
Africa Research Statistics
Google Scholar
Research Gate
Researcher ID
CrossRef
Fuzzy and hard clustering analysis for thyroid disease
Faculty
Science
Year:
2013
Type of Publication:
Article
Pages:
1-16
Authors:
Azar, Ahmad Taher, El-Said, Shaimaa Ahmed, Hassanien, Aboul Ella
DOI:
10.1016/j.cmpb.2013.01.002
Journal:
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE ELSEVIER IRELAND LTD
Volume:
111
Research Area:
Computer Science; Engineering; Medical Informatics
ISSN
ISI:000320346400001
Keywords :
Thyroid disease, K-means clustering, K-medoids clustering, Fuzzy C-means, Gustafson-Kessel algorithm, Gath-Geva algorithm
Abstract:
Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c = 3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set. (C) 2013 Elsevier Ireland Ltd. All rights reserved.
Online
PDF
جامعة المنصورة
جامعة الاسكندرية
جامعة القاهرة
جامعة سوهاج
جامعة الفيوم
جامعة بنها
جامعة دمياط
جامعة بورسعيد
جامعة حلوان
جامعة السويس
شراقوة
جامعة المنيا
جامعة دمنهور
جامعة المنوفية
جامعة أسوان
جامعة جنوب الوادى
جامعة قناة السويس
جامعة عين شمس
جامعة أسيوط
جامعة كفر الشيخ
جامعة السادات
جامعة طنطا
جامعة بنى سويف