Clustering algorithm for the moving or trajectory data provides new and helpful information. It has wide application on various location aware services. In this study the Self Organizing Map is used to form the cluster on trajectory data. The self-organizing map (SOM) is an important tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular gri...

To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model bas...

In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network (SOM). The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to di...

a r t i c l e i n f o The studies of impervious surfaces are important because they are related to many environmental problems, such as water quality, stream health, and the urban heat island effect. Previous studies have discussed that the self-organizing map (SOM) can provide a promising alternative to the multi-layer perceptron (MLP) neural networks for image classification at both per-pixel...

Text Categorization is a process of classifying documents with regard to a group of one or more existent categories [1] according to themes or concepts present in their contents. The most common application of it is in Information Retrieval Systems (IRS) to document indexing [2]. The organization of text in categories allow the user to limit the target of a search submitted to IRS, to explore t...

The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization methods present different aspects of the information learned by the SOM to gain insight and guide segmentation of the data. In this work, we propose a new visualization scheme that represents data topology superimposed o...

Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achi...

The self-organizing map (SOM) has been successfully employed to handle the Euclidean traveling salesman problem (TSP). By incorporating its neighborhood preserving property and the convex-hull property of the TSP, we introduce a new SOM-like neural network, called the expanding SOM (ESOM). In each learning iteration, the ESOM draws the excited neurons close to the input city, and in the meantim...

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...

We generalize a class of neural network models that extend the Kohonen self-organizing map (SOM) algorithm into the sequential and temporal domain using recurrent connections. Behaviour of the class of Activation-based Recursive Self-organizing Maps (ARSOM) is discussed with respect to the choice of transfer function and parameter settings. By comparing performances to existing benchmarks we de...