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ComTech, 2023 The research aimed to use Fuzzy C-Means clustering in content-based document clustering to classify general websites based on their content. The data used were a table ranking of the most visited websites for Indonesia, taken from https://dataforseo.com ...
Sri Probo Aditiyo +2 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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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS
Media Statistika, 2015 Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent ...
Baiq Nurul Haqiqi, Robert Kurniawan
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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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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
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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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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
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Document clustering using sample weighting [PDF]
, 2007 Clustering algorithm based on Sample weighting has been noticed recently. In this paper, a novel sample weighting clustering algorithm is presented based on K-Means and fuzzy C-Means algorithm.
Su, Xinning +2 more
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General fuzzy min-max neural network for clustering and classification [PDF]
, 2000 This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993).
Gabrys, Bogdan, Bargiela, Andrzej
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