Median evidential c-means algorithm and its application to community detection [PDF]
, 2015 Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical ...
Liu, Zhun-Ga +3 more
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Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification
Remote Sensing, 2021 Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images.
Anjali Madhu, Anil Kumar, Peng Jia
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TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM [PDF]
Journal of Process Management and New Technologies, 2014 Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead ...
Samarjit Das, Hemanta K. Baruah
doaj
Fuzzy C-Means Clustering Using Asymmetric Loss Function
Journal of Statistical Theory and Applications (JSTA), 2020 In this work, a fuzzy clustering algorithm is proposed based on the asymmetric loss function instead of the usual symmetric dissimilarities. Linear Exponential (LINEX) loss function is a commonly used asymmetric loss function, which is considered in this
Israa Abdzaid Atiyah +3 more
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AN APPROACH TO REMOVE THE EFFECT OF RANDOM INITIALIZATION FROM FUZZY C-MEANS CLUSTERING TECHNIQUE [PDF]
Journal of Process Management and New Technologies, 2014 Out of the different available fuzzy clustering techniques Bezdek’s Fuzzy C-Means clustering technique is among the most popular ones. Due to the random initialization of the membership values the performance of Fuzzy C-Means clustering technique ...
Samarjit Das, Hemanta K. Baruah
doaj
Применение методов кластеризации для диагностики болезни Альцгеймера на основе ПЕТ-изображений [PDF]
, 2016 Робота присвячена використанню методів кластеризації в системах нечіткого виводу для класифікації ПЕТ-зображень з метою діагностики хвороби Альцгеймера. Оцінені характеристики кожного з трьох представлених кластеризаційних методів: Subtractive Clustering,
Gorriz, Huan Manuel +11 more
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A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data.
J. Arora, M. Tushir
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KC-Means: A Fast Fuzzy Clustering
Advances in Fuzzy Systems, 2018 A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm. The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages.
Israa Abdzaid Atiyah +2 more
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Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]
, 2009 Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Bink, M.C.A.M. +3 more
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Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection
Applied Sciences, 2019 With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data.
Ruonan Zhao, Lize Gu, Xiaoning Zhu
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