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This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian...
Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonenpsilas Self-Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their...
Functional networks are the extension of neural networks which have been studied recently. Like neural networks, there is no systematic method for designing approximation functional network structures. In this paper, a new entropy clustering method designed for functional networks is presented, which combines each neuron function and functional parameters by performing the optimal search to achieve...
Motor fault diagnosis methods are crucial in acquiring safe and reliable operation in motor drive systems. In this paper, we propose a hybrid algorithm for motor fault diagnosis based on the combination of artificial immune system (AIS) and artificial neural network (ANN). The artificial immune algorithm (AIA) for data clustering is employed to adaptively choose the amount and location of the hidden...
A hybrid approach combining the self-organizing map (SOM) and the hidden Markov model (HMM) is presented. The self-organizing hidden Markov model map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are blended together in an attempt to meet the increasing requirements imposed...
Neural networks approach is one of the most promising methodologies for intrusion detection in network security. An integrated intrusion detection system (IIDS) scheme based on multiple neural networks is proposed. The approaches used in IIDS include principal component neural networks, growing neural gas networks and principal component self-organizing map networks. By the abilities of classification...
Because of today's explosive information from Internet, people will contact much new information at any moment. So how to analyze this non-stationary information becomes more and more important. Clustering analysis is a good information analysis method, but many clustering algorithms only fit to stationary situation. Then in this paper, a novel incremental clustering algorithm based on self-organizing-mapping-IGSOM...
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...
After such characteristics as normalization of vector and global vigilance parameter have been analyzed in the clustering process of classical Adaptive Resonance Theory Network (ART2), shortcomings of ART2 are pointed out, which are inapplicability to the situation correlative with vector modulus, inability of dividing space with different granularities according to the densities of space and output...
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...
The self-organizing map (SOM) is an unsupervised neural network approach that reduces a high-dimensional data set to a representative and compact two-dimensional grid. In so doing, a SOM reveals emergent clusters within the data. Research has shown that SOMs lend themselves to visual and computational analysis for exploratory and data mining purposes. However, an important requirement for many SOM...
This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to...
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