GTM: the generative topographic mapping
Neural Computation
Kernel-based equiprobabilistic topographic map formation
Neural Computation
Dynamic topology representing networks
Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel-based topographic map formation by local density modeling
Neural Computation
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Signal Processing - Special issue: Genomic signal processing
Self-Organization of Topographic Mixture Networks Using Attentional Feedback
Neural Computation
Reclassification as Supervised Clustering
Neural Computation
Neural Computation
Data Mining
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
A novel Boolean algebraic framework for association and pattern mining
WSEAS Transactions on Computers
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The extraction of fuzzy association rules for the description of dependencies and interactions from large data sets as those arising in gene expression data analysis applications perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper describes a two stage approach to the problem that obtains computationally manageable solutions. The first stage aims to cluster transactions that more probably are associated. Thereafter, the second stage, the fuzzy association rule extraction follows, confronting a significantly reduced problem. The clustering phase is accomplished by means of a Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). The mutual information metric controls the development of the KSDG-SOM clusters. This metric allows the formation of data clusters that maximize the mutual information for transactions of the same cluster and to minimize it between different clusters. In addition the KSDG-SOM is capable of incorporating a priori information concerning the transaction's items that can focus the model to cluster together even more probably associated items. After this initial data clustering we concetrate on whether the pattern of a transaction can be associated with characteristics of the patterns of other transactions of the same node. Therefore, the fuzzy association rules are extracted locally on a per cluster basis. The paper focuses on the application of the techniques for mining the gene expression data. However, the presented techniques can easily be adapted and can be fruitful for intelligent exploration of any other data set as well.