Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Exploratory analysis of fMRI data by fuzzy clustering: philosophy, strategy, tactics, implementation
Exploratory analysis and data modeling in functional neuroimaging
Globally optimal vector quantizer design by stochastic relaxation
IEEE Transactions on Signal Processing
Fuzzy cluster analysis of high-field functional MRI data
Artificial Intelligence in Medicine
EvIdentTM: a functional magnetic resonance image analysis system
Artificial Intelligence in Medicine
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Clustering quality based feature selection method
Machine Graphics & Vision International Journal
Parallel adaptive simulated annealing for computer-aided measurement in functional MRI analysis
Expert Systems with Applications: An International Journal
ACM Transactions on Computer-Human Interaction (TOCHI)
Artificial Intelligence in Medicine
International Journal of Innovative Computing and Applications
Resource constraints on computation and communication in the brain
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting activated regions of fMRI data using unsupervised learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Clustering of fMRI data using affinity propagation
BI'10 Proceedings of the 2010 international conference on Brain informatics
Exploring functional connectivity networks in fMRI data using clustering analysis
BI'11 Proceedings of the 2011 international conference on Brain informatics
Exploring brain activation patterns during heuristic problem solving using clustering approach
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.