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A modified kohonen self-organizing map (KSOM) clustering for four categorical data

A modified kohonen self-organizing map (KSOM) clustering for four categorical data

T he Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified .
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Neural Network based Clustering using Visual Features of Characters‟ Shape in Image

Neural Network based Clustering using Visual Features of Characters‟ Shape in Image

Abstract -- Clustering gathers similar objects. A Character can also be treated as object and can be recognized in the image through its visual features. In this work, characters of the Urdu script are clustered on the basis of 18 different visual features. A Kohonen Self Organizing Map is used for clustering with four different topologies of sizes 6x5, 8x7, 9x8, and 10x10. Each topology is checked under 75, 100, 150 and 200 numbers of epochs. 30 Urdu characters make 106 different shapes due to the four different positions in the word. These 106 shapes are then classified into 53 general classes based on graphical similarity. The shape of each class comprises features for its description. Considering only 18 features of each shape, 53 general classes are then grouped into clusters using a Kohonen Self Organizing Map (K-SOM). The above mentioned work has been implemented in MATLAB.
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Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps

Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps

The SOM or Kohonen Map is an unsupervised artificial neural network technique and was developed by Teuvo Kohonen (17). It creates a set of prototype vectors representing the data set and carries out a topology preserving projection of the prototypes from high-dimensional input space onto a low-dimensional grid such as 1D or 2D grid of map units. This artificial neural network training involves the process of adjusting weights of neurons to the distributions of the input data. After the training process has been performed, clusters are identified by mapping object to the output neurons. As mentioned in (5), the SOM can be interpreted as a topology preserving mapping from input space onto the 2D grid map units. The elements of the SOM display can be called output neurons, map units or even virtual units, term that has been used in (8) for ecological modelling. The number of output neurons which typically varies from a few dozen up to several thousand usually determines the accuracy and generalization capability of the SOM. The SOM is trained iteratively and as described in (8) (17), the SOM algorithm can be presented in six following steps:
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Hand Gesture Recognition System Using Kohonen Self Organizing Map

Hand Gesture Recognition System Using Kohonen Self Organizing Map

There are three phases in the development of the system as shown in Fig. 7. First is the training phase, second is setting boundaries, and third is the testing phase. In the training phase, ten samples of each hand gestures representing letters A to Z, Space, Backspace and Enter keys were collected from different hand owners. Fig. 6 shows the 29 hand gesture images and the background image. In each phase the five stages of image processing were performed on all images. All of the processed images were concatenated to create a single output before being presented to the SOM. The MATLAB Artificial Neural Network (ANN) Toolbox [3] is utilized. The SOM was set to 20 x 20 dimensions, creating 400 cluster
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Face Recognition Using Neural Network Technique Som (Self Organizing Maps)

Face Recognition Using Neural Network Technique Som (Self Organizing Maps)

Abstract: Face recognition from still images or videos is challenging due to the wide variability of face appearances and the complexity of the image background. Also uncontrolled conditions like illumination, expression and partial occlusion effects face identification and it has become a matter of concern. Faces are detected and recognized at various angles of face expressions. The process of recognition is performed by comparing the characteristics of the target face to that of known individuals face images. In order to overcome above challenges, we propose a Face Recognition System (FRS) that uses Principal Component Analysis (PCA) method for feature extraction and Kohonen Self- Organizing Maps (SOM) algorithm to identify and classify the human face images. The purpose of this study is to increase the speed and accuracy of existing face recognition systems. In this paper, we first discusses the characteristics of Kohonen self- organizing maps to highlight the advantages and disadvantages of neural networks in classifications approaches. In the second part, data used for classification of target images, obtained from publically available databases is pre- processed using appropriate methods using MATLAB software. The result of these computations is database, composed of publically available daily load images, used for SOM training. In the third part, the proposed software is tested on few scenarios in order to classify different face images. With the usage of SOM technique along with other neural networks, the system’s accuracy is increased to 97%, with face detection rate of 100% and recognition rate of 86.84%, which is higher than existing algorithms.
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Landforms identification using neural network-self organizing map and SRTM data

Landforms identification using neural network-self organizing map and SRTM data

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales. In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish - Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis. Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines. Results showed that Self Organizing Map is a very promising and efficient tool for land form identification. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements.
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New variant of the Self Organizing Map in Pulsed Neural Networks to Improve Phoneme Recognition in Continuous Speech

New variant of the Self Organizing Map in Pulsed Neural Networks to Improve Phoneme Recognition in Continuous Speech

Spiking neural models can account for different types of computations, ranging from linear temporal summation of inputs and coincidence detection to multiplexing, nonlinear operations and preferential resonance [9]. Several recent studies employing rigorous mathematical tools have demonstrated that through the use of temporal coding, a pulsed neural network may gain more computational power than a traditional network (i.e., consisting of rate coding neurons) of comparable size [10]. A simple spiking neural model can carry out computations over the input spike trains under several different modes [9]. Thus, spiking neurons compute when the input is encoded in temporal patterns, firing rates, firing rates and temporal correlations, and space–rate codes. An essential feature of the spiking neurons is that they can act as coincidence detectors for the incoming pulses, by detecting if they arrive in almost the same time [11] [12]. In the following, we present the self-organizing map of Kohonen (SOM), then, we present some temporal self- organizing map models, thereafter we propose the Leaky Integrator neurons model (LIN). Finally, we illustrate experimental results of the application of SOM variant in phoneme classification.
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Kohonen Self Organizing for Automatic Identification of Cartographic Objects

Kohonen Self Organizing for Automatic Identification of Cartographic Objects

An important basic principle is that the features must be independent of class membership because, by definition, at the feature extraction phase the membership in the classes is not yet known. This implies that any learning methods used for feature extraction should be unsupervised in the sense that the target class for each object is unknown [9]. One of the approaches is the Kohonen Self Organizing Map (KSOM) that uses competitive learning, which in turn results in data clustering [8]. The KSOM belongs to the class of unsupervised neural networks based on competitive learning, in which only one output neuron, or one per local group of neurons at a time gives the active response to the current input signal. The level of activity indicates the similarity between the input signal vector and its respective weight vector. A standard way of expressing similarity is through the Euclidean distance between these vectors. Since the distance between the weight vector of a given neuron and the input data vector is minimal for all neurons in the network, a neuron together with a predefined set of neighbor neurons will have their weights automatically updated by the learning algorithm. The neighborhood for each neuron may be defined accordingly to the geometrical form, over which the neurons are arranged. Figure 3
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Image Compression using Neural Network

Image Compression using Neural Network

Image compression algorithm is needed that will reduce the amount of Image to be transmitted, stored and analyzed, but without losing the information content. This paper presents a neural network based technique that may be applied to image compression. Conventional techniques such as Huffman coding and the Shannon Fano method, LZ Method, Run Length Method, LZ-77 are more recent methods for the compression of data. A traditional approach to reduce the large amount of data would be to discard some data redundancy and introduce some noise after reconstruction. We present a neural network based self-organizing Kohonen map technique that may be a reliable and efficient way to achieve vector quantization. Typical application of such algorithm is image compression. Moreover, Kohonen networks realize a mapping between an input and an output space that preserves topology. This feature can be used to build new compression schemes which allow obtaining better compression rate than with classical method as JPEG without reducing the image quality.
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Weight Optimize by Automatic Unsupervised Clustering using Computation Intelligence

Weight Optimize by Automatic Unsupervised Clustering using Computation Intelligence

The weight determination of neural network is very significant and necessary because it is the first step of algorithm calculation. Actually, the weight determination must be stemmed from learning suitable values obtained from system practice or expert’s experience in specific fields. The optimized weight value will affect a good and precise output of the system. Nevertheless, it is quite difficult to optimize the appropriate value because it does not mean that the proper value will be suitable for all kinds of data. On the other hand, it should be the appropriate value for the data used for analysis. The weight must be adjustable and flexible following the data while the required unsupervised clustering algorithm must apply the weight value with input pattern to process the outcome, for instance, Kohonen’s Winner-Take- All’s network,Kohonen self-organizing feature maps and incremental learning fuzzy neural network.
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Molecular subtyping of bladder cancer using Kohonen self-organizing maps

Molecular subtyping of bladder cancer using Kohonen self-organizing maps

through molecular biology) or by alternate methods of analysis. With this in mind, we have undertaken this study to evaluate the ability of SOM to integrate clinical– molecular information for stratifying outcomes in BC. Traditionally, statistical techniques such as Cox’s propor- tional hazards and logistic regression are usually employed when analyzing prognostic information. Classic statistical modeling requires the explicit assumption of certain relationships within the data that are often unpro- ven. ANNs offer a number of theoretical advantages, including ability to detect complex nonlinear relationships between variables, ability to detect all possible interactions between predictor variables, and the availability of multi- ple training algorithms [27]. The ANN techniques depicted in the literature can be mainly categorized under two headings: supervised and unsupervised. Kohonen SOM consists in a feed forward neural network that uses an unsupervised training (partitional clustering). It means that, the data are directly divided into a set of clusters without any regard to the relationships between the clus- ters. These methods try to maximize some measure of similarity within the units (patients) of each cluster,
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An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM

An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM

-------------------------------------------------------------------------ABSTRACT ------------------------------------------------------------- This paper presents a new approach of Kohonen neural network based Self Organizing Map (SOM) algorithm for Tamil Character Recognition. Which provides much higher performance than the traditional neural network. Approaches: Step 1: It describes how a system is used to recognize a hand written Tamil characters using a classification approach. The aim of the pre-classification is to reduce the number of possible candidates of unknown character, to a subset of the total character set. This is otherwise known as cluster, so the algorithm will try to group similar characters together. Step 2: Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.
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Filtered Wall: An Automated System to Filter Unwanted Messages from OSN User Walls

Filtered Wall: An Automated System to Filter Unwanted Messages from OSN User Walls

interpolation [13], and are demonstrated to have the universal approximation property [14], [15]. As outlined in [16], RBFN main advantages are that classification function is nonlinear, the model may produce confidence values and it may be robust to outliers; drawbacks are the potential sensitivity to input parameters, and potential overtraining sensitivity. The first-level classifier is then structured as a regular RBFN with Self Organizing Neural Network (SOINN). In the second level of the classification stage, we introduce a modification of the standard use of RBFN with Self Organizing Neural Network (SOINN). Its regular use in classification includes a hard decision on the output values: according to the winner-take-all rule, a given input pattern is assigned with the class corresponding to the winner output neuron which has the highest value. In our approach, we consider all values of the output neurons as a result of the classification task and we interpret them as gradual estimation of multimember ship to classes.
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Image Segmentation using Bi Directional Self          Organize Neural Network (BDSONN)

Image Segmentation using Bi Directional Self Organize Neural Network (BDSONN)

neighborhood fuzzy subsets in the image information are propagated to the succeeding layers of the network using the fixed and full inter-layer interconnections between the corresponding neurons of the different layers of the network. In this way, the network input states are propagated from the input layer to the output layer of the network. The backward path inter-layer connection strengths from the output layer to the intermediate layer are again evaluated from the relative measures of the fuzzy membership values at the output layer neurons. The output layer network states and the corresponding output layer neighborhood context information are propagated to the intermediate layer through the backward path inter-layer connections for further processing. This to and from propagation of the network states between the two inner layers of the network architecture is continued until the inter-layer connection strengths from the intermediate layer to the output layer and back stabilize. At this point, the fuzzy hostility indices, which are reflective of the heterogeneity of the image information content, are reduced to minimum and the original input image information is self supervised into homogeneous object and background regions at the network output layer.
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A Computerized Neural Network System to Filter Unwanted Messages from OSN User Walls

A Computerized Neural Network System to Filter Unwanted Messages from OSN User Walls

The Filtered wall architecture in support of OSN services is a three-tier structure (see Fig. 1). The first layer, called Social Network Manager (SNM), it provides the basic OSN functionalities (i.e., profile and relationship management), whereas the second layer provides the support for external Social Network Applications (SNAs). The supported SNAs may in turn require an additional layer for their needed Graphical User Interfaces (GUIs). According to this reference architecture, the proposed system is placed in the second and third layers. In particular, users interact with the system by means of a GUI to set up and manage their FRs/ BLs. Moreover, the GUI provides users with a FW, that is, a wall where only
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Research on the Status of Wireless Self organizing Network

Research on the Status of Wireless Self organizing Network

The planar distributed architecture is the most common architecture of wireless ad hoc networks. As shown in Figure .1, the relationship between each node in a planar distributed network architecture is equal and there is no central control node. This structure has a strong robustness, management is relatively simple. But once the number of nodes in the network continue to grow exponentially, then the maintenance of the entire network routing information will increase exponentially, this time will cause insufficient memory, management overhead is too large, the network is relatively low scalability phenomenon. This architecture is more suitable for small and medium-sized network structure.
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Towards model evaluation and identification using Self Organizing Maps

Towards model evaluation and identification using Self Organizing Maps

Abstract. The reduction of information contained in model time series through the use of aggregating statistical perfor- mance measures is very high compared to the amount of in- formation that one would like to draw from it for model iden- tification and calibration purposes. It has been readily shown that this loss imposes important limitations on model identifi- cation and -diagnostics and thus constitutes an element of the overall model uncertainty. In this contribution we present an approach using a Self-Organizing Map (SOM) to circumvent the identifiability problem induced by the low discrimina- tory power of aggregating performance measures. Instead, a Self-Organizing Map is used to differentiate the spectrum of model realizations, obtained from Monte-Carlo simulations with a distributed conceptual watershed model, based on the recognition of different patterns in time series. Further, the SOM is used instead of a classical optimization algorithm to identify those model realizations among the Monte-Carlo simulation results that most closely approximate the pattern of the measured discharge time series. The results are an- alyzed and compared with the manually calibrated model as well as with the results of the Shuffled Complex Evo- lution algorithm (SCE-UA). In our study the latter slightly outperformed the SOM results. The SOM method, however, yields a set of equivalent model parameterizations and there- fore also allows for confining the parameter space to a region that closely represents a measured data set. This particular feature renders the SOM potentially useful for future model identification applications.
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How to Get the Same News from Different Language News Papers

How to Get the Same News from Different Language News Papers

In the past decade there has been significant amount of work done on finding similarity of documents and organizing the documents according to their content. Similarity of documents are identified using different methods such as Self-Organizing Maps (SOMs) (Kohonen et al, 2000; Rauber, 1999), based on Ontologies and taxanomy (Gruber, 1993; Resnik, 1995), Vector Space Model (VSM) with similarity measures like Dice similarity, Jaccard’s similarity, cosine similarity (Salton, 1989).

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An Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network

An Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network

consuming section and it needs to be designed properly; therefore it is possible for the decision- making part to act properly in this real-time environment. The cause of this problem is that the transducer part includes many functions that have to act simultaneously to produce some linearly separable data. Functions such as, distance to the centre of the rival’s goal, agent’s density in the penalty region, view angle for ball owner and all other teammates in the penalty region. The worst function among these functions is the confidence function, because it needs to calculate many characteristics in the field. When transducer block produces a state vector, it goes to the Trained Kohonen block, which has been trained by 500 data (the others have been kept to test the performance of the system) to converge to the proper cluster for this state vector by its max block. After training mode, the weighting matrix of the Kohonen network for the attacker agent is:
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A Survey on Brain Tumor Detection and Classification Techniques

A Survey on Brain Tumor Detection and Classification Techniques

Yudong Zhang, Zhengchao Dong, Lenan Wua, Shuihua Wanga [6], have developed a novel hybrid classifier to distinguish normal and abnormal brain MRIs. In this paper, they present a neural network (NN) based method to classify a given MR brain image as normal or abnormal. This method first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to a back propagation (BP) NN, with which scaled conjugate gradient (SCG) is adopted to find the optimal weights of the NN.
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