An efficient fuzzy C-least median clustering algorithm

In today’s reality ’World Wide Web’ is considered as the archive of extremely enormous measure of data. The substance and complexity of WWW are increasing day by day. Presently the circumstances are such that we are suffocating in data yet starving for knowledge. Because of these circumstances da...

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Main Authors: Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Aboosalih, K C
Format: Article
Language:English
Published: IOP Publishing Ltd 2021
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Online Access:http://irep.iium.edu.my/89449/7/89449_An%20efficient%20fuzzy%20C-least%20median%20clustering%20algorithm.pdf
http://irep.iium.edu.my/89449/
https://iopscience.iop.org/article/10.1088/1757-899X/1070/1/012050/pdf
https://doi.org/10.1088/1757-899X/1070/1/012050
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Institution: Universiti Islam Antarabangsa Malaysia
Language: English
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spelling my.iium.irep.894492021-04-21T02:16:35Z http://irep.iium.edu.my/89449/ An efficient fuzzy C-least median clustering algorithm Mallik, Moksud Alam Zulkurnain, Nurul Fariza Nizamuddin, Mohammed Khaja Aboosalih, K C TK7885 Computer engineering In today’s reality ’World Wide Web’ is considered as the archive of extremely enormous measure of data. The substance and complexity of WWW are increasing day by day. Presently the circumstances are such that we are suffocating in data yet starving for knowledge. Because of these circumstances data mining is extremely important to get valuable data from WWW.Clustering data mining is the process of putting together meaning-full or use-full similar object into one group. It is a common technique for statistical data, machine learning and computer science analysis. Clustering is a kind of unsupervised data mining technique which describes general working behavior, pattern extraction and extracts useful information from time series data. In this paper we are discussing our new procedure for clustering called Fuzzy C-least median of squares algorithm which is an improvement to Fuzzy C-means (FCM) algorithm. As it is concerned with the least value among medians, it wipes out means squared error and eliminates the effect of outliers. We compared our clustering result got by applying FCM and FCLM by using Xie-Beni Index, Fukuyama-Sygeno Index and Partition Coefficient. The outcomes demonstrate a clear improvement of our algorithm than existing FCM algorithm. IOP Publishing Ltd 2021 Article PeerReviewed application/pdf en http://irep.iium.edu.my/89449/7/89449_An%20efficient%20fuzzy%20C-least%20median%20clustering%20algorithm.pdf Mallik, Moksud Alam and Zulkurnain, Nurul Fariza and Nizamuddin, Mohammed Khaja and Aboosalih, K C (2021) An efficient fuzzy C-least median clustering algorithm. IOP Conference Series: Materials Science and Engineering, 1070. pp. 1-11. ISSN 1757-8981 E-ISSN 1757-899X https://iopscience.iop.org/article/10.1088/1757-899X/1070/1/012050/pdf https://doi.org/10.1088/1757-899X/1070/1/012050
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Mallik, Moksud Alam
Zulkurnain, Nurul Fariza
Nizamuddin, Mohammed Khaja
Aboosalih, K C
An efficient fuzzy C-least median clustering algorithm
description In today’s reality ’World Wide Web’ is considered as the archive of extremely enormous measure of data. The substance and complexity of WWW are increasing day by day. Presently the circumstances are such that we are suffocating in data yet starving for knowledge. Because of these circumstances data mining is extremely important to get valuable data from WWW.Clustering data mining is the process of putting together meaning-full or use-full similar object into one group. It is a common technique for statistical data, machine learning and computer science analysis. Clustering is a kind of unsupervised data mining technique which describes general working behavior, pattern extraction and extracts useful information from time series data. In this paper we are discussing our new procedure for clustering called Fuzzy C-least median of squares algorithm which is an improvement to Fuzzy C-means (FCM) algorithm. As it is concerned with the least value among medians, it wipes out means squared error and eliminates the effect of outliers. We compared our clustering result got by applying FCM and FCLM by using Xie-Beni Index, Fukuyama-Sygeno Index and Partition Coefficient. The outcomes demonstrate a clear improvement of our algorithm than existing FCM algorithm.
format Article
author Mallik, Moksud Alam
Zulkurnain, Nurul Fariza
Nizamuddin, Mohammed Khaja
Aboosalih, K C
author_facet Mallik, Moksud Alam
Zulkurnain, Nurul Fariza
Nizamuddin, Mohammed Khaja
Aboosalih, K C
author_sort Mallik, Moksud Alam
title An efficient fuzzy C-least median clustering algorithm
title_short An efficient fuzzy C-least median clustering algorithm
title_full An efficient fuzzy C-least median clustering algorithm
title_fullStr An efficient fuzzy C-least median clustering algorithm
title_full_unstemmed An efficient fuzzy C-least median clustering algorithm
title_sort efficient fuzzy c-least median clustering algorithm
publisher IOP Publishing Ltd
publishDate 2021
url http://irep.iium.edu.my/89449/7/89449_An%20efficient%20fuzzy%20C-least%20median%20clustering%20algorithm.pdf
http://irep.iium.edu.my/89449/
https://iopscience.iop.org/article/10.1088/1757-899X/1070/1/012050/pdf
https://doi.org/10.1088/1757-899X/1070/1/012050
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