Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6114
Title: A new Fuzzy Noise-rejection Data Partitioning Algorithm with Revised Mahalanobis Distance
Authors: Zarandi, Mohammad Hossein Fazel
Avazbeigi, Milad
Türkşen, İsmail Burhan
Keywords: Cluster Validity Index (CVI)
Fuzzy C-Means (FCM)
Possibilistic C-means (PCM)
Revised Gustafson-Kessel (GK)
Revised Mahalanobis Distance
Issue Date: 2009
Publisher: European Soc Fuzzy Logic & Technology
Source: Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) -- JUL 20-24, 2009 -- Lisbon, PORTUGAL
Abstract: Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanobis distance is presented which is able to detect the noise and outlier data and also ellipsoidal clusters. Unlike the traditional FCM, the proposed clustering tool provides much efficient data partitioning capabilities in the presence of noise and outliers. For validation of the proposed model, the model is applied to different noisy data sets.
URI: https://hdl.handle.net/20.500.11851/6114
ISBN: 978-989-95079-6-8
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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