Applying Fuzzy Transformed C-Means Algorithm to the Dermatology Disease and Iris Clustering

ASIA unversity > 資訊學院 > 生物資訊與醫學工程學系 > 博碩士論文 > Item 310904400/2633

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題名:	Applying Fuzzy Transformed C-Means Algorithm to the Dermatology Disease and Iris Clustering
作者:	YI-HSIANG LO
貢獻者:	Department of Bioinformatics
關鍵詞:	FCM- algorithm
日期:	2009
上傳時間:	2009-11-06 14:33:07 (UTC+0)
出版者:	Asia University
摘要:	Dermatological disease is common in places that are humid,damp, and hot like Taiwan, various reasons can cause skin diseases with which the symptoms are varied. In this study, six types of skin diseases experimental data, iris data synchronization also conduct experiments to verify the accuracy of the experiment. The popular fuzzy c-means algorithm (FCM) proposed by Bezdek in 1981is an objective function based clustering method. Hence, different objective function may lead to different results. The important issue is how to get a more compact and separable objective function to improve the cluster accuracy. The objective function of the well known improved algorithm, FCS proposed by G. L. Lee et al in 2005, is a generalization of the FCM objective function by combining fuzzy within- and between-cluster variations. In this paper, a more separable data transformation, the improved new algorithm, “Fuzzy Transformed C-Means algorithm(FTCM)”, is proposed by Hsiang-Chuan Liu in 2009. Two real data sets were applied to prove that the performance of the FTCM algorithm is better than the conventional FCM algorithm and the FCS algorithm. In case study, artificial intelligence technology was able to provide a diagnostic tool for physician or to help patients with self-check before treatment; it could avoids unnecessary waste of medical resources and improves the quality of medical services.
顯示於類別:	[生物資訊與醫學工程學系 ] 博碩士論文

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