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...

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

Ce papier présente un modèle génératif et son estimation permettant la visualisation de données binaires. Notre approche est basée sur un modèle de mélange de lois de Bernoulli par blocs et les cartes de Kohonen probabilistes. La méthode obtenue se montre à la fois parcimonieuse et pertinente en pratique.

In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknes...

Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear particle analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self organizing maps. This is achieved using relationship measuremen...

Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear debris analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self-organizing maps. This is achieved using relationship measurements...

This paper presents an innovative, adaptive variant of Kohonen’s selforganizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on the best architecture for the self-organizing map. Like the traditional SOMs, this clustering technique also provides useful information about the relationship between the resulting clusters. Applications of the resulting softwar...

In this paper we propose a new architecture for speaker recognition. This architecture is independent of the text, robust with the presence of noise, and is based on the Self Organizing Maps (SOM) [I]. We compare the performance of this architectue for different parameuizations, different signal to noise ratios, with another method for speaker identification based on the arithmetic-harmonic sph...

Lexicon is a central component in any language processing system, whether human or artificial. Recent empirical evidence suggests that a multilingual lexicon consists of a single component representing word meanings, and separate component for the symbols in each language. These components can be modeled as self-organizing maps, with associative connections between them implementing comprehensi...