A chaotic self-organizing map can be produced by replacing the linear neural units of the conventional selforganizing map with neural units capable of producing chaos. The introduction of chaos into the selforganizing map is shown to improve the ability of the network to cluster input patterns. output converges to a single non-zero value. As g is increased beyond 0.75 the output begins to oscil...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional measurement data into simple geometric relationships, usually on a two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data eleme...

In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The similarity measure is composed from two terms: weighted Ward distance and Euclidean distance weighted by neighbourhood function. (2) An algorithm inspired from artificial ants named AntTree will be used to cluster a self-organizin...

Privacy-preserving data mining seeks to allow the cooperative execution of data mining algorithms while preserving the data privacy of each party concerned. In recent years, many data mining algorithms have been enhanced with privacy-preserving feature: decision tree induction, frequent itemset counting, association analysis, k-means clustering, support vector machine, Näıve Bayes classifier, B...

The self-organizing map is one of the most prominent unsupervised learning architectures used to visualize the similarities of high-dimensional input structures. What remains by no means straightforward , is an explicit representation of cluster boundaries in the nal two-dimensional map display. The detection of these boundaries rather requires some amount of insight into the inherent structure...

A Self-Organizing Map was used to classify the Reuters Corpus, by assigning a label to each of the documents that cluster to a specific node in the Self-Organizing Map. The predicted label is based on the most frequent label among the training documents attributed to that particular node. Experiments were carried out on different grid sizes (node numbers) to determine their influence on classif...

over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...