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PR
2007
340views more  PR 2007»
13 years 4 months ago
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
— Fuzzy c-means (FCM) algorithms with spatial constraints (FCM_S) have been proven effective for image segmentation. However, they still have the following disadvantages: 1) Alth...
Weiling Cai, Songcan Chen, Daoqiang Zhang
TIP
2010
103views more  TIP 2010»
12 years 11 months ago
A Robust Fuzzy Local Information C-Means Clustering Algorithm
Stelios Krinidis, Vassilios Chatzis
FUIN
2011
358views Cryptology» more  FUIN 2011»
12 years 8 months ago
Unsupervised and Supervised Learning Approaches Together for Microarray Analysis
In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as ...
Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopa...
IJIS
2007
155views more  IJIS 2007»
13 years 4 months ago
Clustering web search results using fuzzy ants
Algorithms for clustering web search results have to be efficient and robust. Furthermore they must be able to cluster a dataset without using any kind of a priori information, s...
Steven Schockaert, Martine De Cock, Chris Cornelis...
BMVC
1998
13 years 5 months ago
A Method for Dynamic Clustering of Data
This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Ko...
Arnaldo J. Abrantes, Jorge S. Marques