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In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an...
The Kohonen self organizing map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered...
The Kohonen self organizing map is widely used as a popular tool in the exploratory phase of data mining. The SOM (self organizing maps) maps high dimensional space into a 2-dimensional grid by placing similar elements close together, forming clusters. Recently research experiments presented that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries...
A novel approach for the classification of compressed video data using centroid neural network with Bhattacharyya kernel (CNN(BK)) is proposed in this paper. The proposed classifier is based on centroid neural network (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, since the feature vectors of compressed video data...
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an clusters that really exist in the empirical data.
A novel approach for the extraction of rectangular boundaries from aerial image data is proposed and presented in this paper. In this approach, a centroid neural network (CNN) with a metric of line segments is also proposed for connecting low-level linear structures or grouping similar objects. Extracting rectangular boundaries for building rooftops from an edge image without height information of...
The self-organizing map (SOM) is an effective method for topologically mapping datasets. By adapting the neurons to the inputs, the network can conform to the data and form clusters. However, with the classical SOM and growing architectures such as growing cells and growing grid, the neurons are simply points in space and do not cover entire regions of the input space. Therefore, inputs that are introduced...
Text clustering is one of the difficult and hot research fields in the Internet search engine research. Combination the advantages of k-means clustering and self-organizing model (SOM) techniques, a new text clustering algorithm is presented. Firstly, texts are preprocessed to satisfy succeed process. Then, the paper analyzes common k-means clustering algorithm and SOM algorithm and combines them...
Conventional clustering techniques provide a static snapshot of each vectorpsilas commitment to every group. With additive datasets, however, existing methods may not be sufficient for adapting to the presence of new clusters or even the merging of existing data-dense regions. To overcome this deficit, we explore the use of growing neural gas for temporal clustering and provide evidence that this...
In this paper, a novel two level HSOM algorithm for clustering information and communication technology (ICT) indicators is presented. The purpose of this research study is to analyze the twenty districts ICT data of Bhutan using HSOM clustering algorithm. The HSOM possesses a two-level hybrid connectionist architecture that comprises (i) an agglomerative hierarchical clustering algorithm to create...
A new algorithm of Web text clustering mining is presented, which is based on the Discovery Feature Sub-space Model (DFSSM). This algorithm includes the training stage of SOM and the clustering stage, which characterizes self-stability and powerful antinoise ability. It can distinguishes the most meaningful features from the Concept Space without the evaluation function. we have applied the algorithm...
A key challenge of data mining is to tackling the problem of mining richly structured datasets such as Web pages. In this paper, we propose a Web text clustering algorithm (WTCA) based on DFSSM, which is our original work. The algorithm includes the training stage of SOM and the clustering stage. It can distinguish the most meaningful features from the Concept Space without the evaluation function...
In order to reduce dimension number of feature space and improve clustering precision, a novel SOM clustering algorithm based on feature selection-FSSOM is provided in this paper. This algorithm first evaluates importance and distinguishing ability of each feature, and only selects features which can efficiently improve clustering precision to construct feature space. Then, it computes kullback-leibler...
A two level clustering approach has been proposed in this paper in order to perform a classification analysis of meteorological data of Annaba region (North-East of Algeria) using data from 1995 to 1999. The Kohonen self-organizing map (SOM) has been used to group the data and produce the meteorological prototypes. The number of prototypes of SOM is large, to facilitate quantitative analysis of the...
Determining the optimum number of clusters is an ill posed problem for which there is no simple way of knowing that number without a priori knowledge. The purpose of this paper is to provide a simultaneous two-level clustering algorithm based on self organizing map, called DS2L-SOM, which learn at the same time the structure of the data and its segmentation. The algorithm is based both on distance...
This study proposes a batch-learning self-organizing map with false-neighbor degree between neurons (called BL-FNSOM). False-neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false-neighbor degrees act as a burden of the distance between map...
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