Results 231 to 240 of about 15,044 (262)
Some of the next articles are maybe not open access.
Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986This paper reports the results of a numerical comparison of two versions of the fuzzy c-means (FCM) clustering algorithms. In particular, we propose and exemplify an approximate fuzzy c-means (AFCM) implementation based upon replacing the necessary ``exact'' variates in the FCM equation with integer-valued or real-valued estimates.
Robert L. Cannon +2 more
openaire +4 more sources
Parallel Fuzzy c-Means Cluster Analysis
2007This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters' centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for the data, by using the PBM validity index to evaluate the ...
Marta V. Modenesi +3 more
openaire +2 more sources
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters, 1998Summary: A new cluster validity index is introduced, which assesses the average compactness and separation of fuzzy partitions generated by the fuzzy \(c\)-means algorithm. To compare the performance of this new index with a number of known validation indices, the fuzzy partitioning of two data sets was carried out.
M. Ramze Rezaee +2 more
openaire +1 more source
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sushmita Mitra +2 more
openaire +3 more sources
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing, 2010Median clustering is a powerful methodology for prototype based clustering of similarity/dissimilarity data. In this contribution we combine the median c-means algorithm with the fuzzy c-means approach, which is only applicable for vectorial (metric) data in its original variant.
Tina Geweniger +3 more
openaire +2 more sources
Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering
2015The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers.
P. Gopala Krishna, D. Lalitha Bhaskari
openaire +1 more source
An Evolutionary Approach to Spatial Fuzzy c-Means Clustering
Fuzzy Optimization and Decision Making, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DI NOLA, Antonio +2 more
openaire +2 more sources
On tolerant fuzzy c-means clustering and tolerant possibilistic clustering
Soft Computing, 2009This paper presents two new types of clustering algorithms by using tolerance vector called tolerant fuzzy c-means clustering and tolerant possibilistic clustering. In the proposed algorithms, the new concept of tolerance vector plays very important role.
Yukihiro Hamasuna +2 more
openaire +1 more source
Fuzzy c-Means Clustering with Discriminative Projection
2021 IEEE International Conference on Big Knowledge (ICBK), 2021Wenjun Wu +5 more
openaire +2 more sources
Online Classifiers Based on Fuzzy C-means Clustering
2013In the online approach a classifier is, as usual, induced from the available training set. However, in addition, there is also some adaptation mechanism providing for a classifier evolution after the classification task has been initiated and started. In this paper two algorithms for online learning and classification are considered.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
openaire +2 more sources