Self-organizing maps
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Neural Networks - Special issue: Neuroinformatics
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection
Artificial Intelligence in Medicine
Effective classification using feature selection and fuzzy integration
Fuzzy Sets and Systems
Scopira: an open source C++ framework for biomedical data analysis applications
Software—Practice & Experience
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
A quantitative comparison of functional MRI cluster analysis
Artificial Intelligence in Medicine
A novel, direct spatio-temporal approach for analyzing fMRI experiments
Artificial Intelligence in Medicine
Object detection in video using Lorenz information measure and discrete wavelet transform
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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EvIdent(TM) (EVent IDENTification) is a user-friendly, algorithm-rich, exploratory data analysis software for quickly detecting, investigating, and visualizing novel events in a set of images as they evolve in time and/or frequency. For instance, in a series of functional magnetic resonance neuroimages, novelty may manifest itself as neural activations in a time course. The core of the system is an enhanced variant of the fuzzy c-means clustering algorithm. Fuzzy clustering obviates the need for models of the underlying requisite biological function, models that are often statistically suspect.