Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
NEC: a hierarchical agglomerative clustering based on fisher and negentropy information
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Robust Clustering by Aggregation and Intersection Methods
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
An Algorithm to Assess the Reliability of Hierarchical Clusters in Gene Expression Data
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Interactive Visualization Tools for Meta-Clustering
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Metaclustering and Consensus Algorithms for Interactive Data Analysis and Validation
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data
Computational Intelligence Methods for Bioinformatics and Biostatistics
Multiple data structure discovery through global optimisation, meta clustering and consensus methods
International Journal of Knowledge Engineering and Soft Data Paradigms
A stability-based algorithm to validate hierarchical clusters of genes
International Journal of Knowledge Engineering and Soft Data Paradigms
Global optimization, meta clustering and consensus clustering for class prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
International Journal of Approximate Reasoning
Comparison of dispersion models by using fuzzy similarity relations
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
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In this work a comprehensive multi-step machine learning data mining and data visualization framework is introduced. The different steps of the approach are: preprocessing, clustering, and visualization. A preprocessing based on a Robust Principal Component Analysis Neural Network for feature extraction of unevenly sampled data is used. Then a Probabilistic Principal Surfaces approach combined with an agglomerative procedure based on Fisher's and Negentropy information is applied for clustering and labeling purposes. Furthermore, a Multi-Dimensional Scaling approach for a 2-dimensional data visualization of the clustered and labeled data is used. The method, which provides a user-friendly visualization interface in both 2 and 3 dimensions, can work on noisy data with missing points, and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Analysis and identification of genes periodically expressed in a human cancer cell line (HeLa) using cDNA microarrays is carried out as test case.