Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz

There are many techniques of clustering the image. The most widely used of clustering technique is Fuzzy C-Means algorithm (FCM). FCM is a technique that allows one piece of data to belong to two or more clusters. The important issues in clustering the image are to determine the optimal number of th...

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Main Author:	Aziz, Azwani
Format:	Thesis
Language:	English
Published:	2010
Online Access:	https://ir.uitm.edu.my/id/eprint/64099/1/64099.PDF
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id	my-uitm-ir.64099
record_format	uketd_dc
spelling	my-uitm-ir.640992023-08-29T09:17:38Z Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz 2010 Aziz, Azwani There are many techniques of clustering the image. The most widely used of clustering technique is Fuzzy C-Means algorithm (FCM). FCM is a technique that allows one piece of data to belong to two or more clusters. The important issues in clustering the image are to determine the optimal number of the clusters. This problem can be solved by cluster validity index. Cluster validity index is needed to find the suitable number of cluster, c in any fuzzy clustering algorithm. So the best cluster validity index must be chosen to obtain the suitable number of cluster. In this project, the best validity indexes that have been chosen are Partition Entropy and Adaptive cluster validity index. Partition Entropy is the most frequently used cluster validity index. In most of fuzzy cluster validity indexes, the separation measures are calculation based on the distances among cluster centers. However, the calculation is based only on centroids information and does not consider the overall cluster shape. So, Adaptive cluster validity index introduce the lattice degree of approaching to overcome this problem and this cluster validity index can be adapted to different type of cluster shape. 2010 Thesis https://ir.uitm.edu.my/id/eprint/64099/ https://ir.uitm.edu.my/id/eprint/64099/1/64099.PDF text en public degree Universiti Teknologi Mara (UiTM) Faculty of Computer and Mathematical Sciences Embong, Rohana
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Embong, Rohana
description	There are many techniques of clustering the image. The most widely used of clustering technique is Fuzzy C-Means algorithm (FCM). FCM is a technique that allows one piece of data to belong to two or more clusters. The important issues in clustering the image are to determine the optimal number of the clusters. This problem can be solved by cluster validity index. Cluster validity index is needed to find the suitable number of cluster, c in any fuzzy clustering algorithm. So the best cluster validity index must be chosen to obtain the suitable number of cluster. In this project, the best validity indexes that have been chosen are Partition Entropy and Adaptive cluster validity index. Partition Entropy is the most frequently used cluster validity index. In most of fuzzy cluster validity indexes, the separation measures are calculation based on the distances among cluster centers. However, the calculation is based only on centroids information and does not consider the overall cluster shape. So, Adaptive cluster validity index introduce the lattice degree of approaching to overcome this problem and this cluster validity index can be adapted to different type of cluster shape.
format	Thesis
qualification_level	Bachelor degree
author	Aziz, Azwani
spellingShingle	Aziz, Azwani Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
author_facet	Aziz, Azwani
author_sort	Aziz, Azwani
title	Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
title_short	Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
title_full	Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
title_fullStr	Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
title_full_unstemmed	Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz
title_sort	cluster validity of the fuzzy c-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / azwani aziz
granting_institution	Universiti Teknologi Mara (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/64099/1/64099.PDF
_version_	1783735397900091392

Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz

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