We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the ...

Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires the development of stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In this paper we present the Growing Hierarchical Self-Organizing Map (GH-SOM), a neural network model based on the self-organizing map. The main f...

In this paper we demonstrate that the Self-Organizing Maps of Kohonen can be used as speech feature ex-tractors that are able to take temporal context into account. We have investigated two alternatives to use SOMs as such feature extractors, one based on tracing the location of highest activity on a SOM, the other on integrating the activity of the whole SOM for a period of time. The experimen...

In this work, Self Organizing Map (SOM) is used in order to detect and classify the broken rotor bars and misalignment type mechanical faults that often occur in induction motors which are widely used in industry. The feature vector samples are extracted from the sampled line current of motors with fault and healthy one. These samples are the poles of the AR model which is obtained from the spe...

Abstract Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary effectively manage resources. We develope unsupervised modeling approach characterize cluster hydrographs on regional scale according dynamics. apply feature-based improve the exploitation heterogeneous ...

Time information of the input data is used for evaluating the goodness of the Self-Organizing Map to store and represent temporal feature vector sequences. A new node neighborhood is defined for the map which takes the temporal order of the input samples into account. A connection is created between those two map nodes which are the best-matching units for two successive input samples in time. ...

In this paper, an innovative method for estimating mass functions using Kohonen’s Self Organizing Map is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. This new method is of interest for solving estimation mass functions problems where a large quantity of multi-variate data is available. Ind...

In this paper we analyze the convergence properties of a class of self-organizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally a ne feature map. A new integrally distributed self-organizing learning law which converges to the equiprobable feature map for inputs which have arbitrary random pr...