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This paper mainly research on the application of the particle swarm optimization algorithm in anomaly detection, including the PSO algorithm combined with clustering method, PSO with neural networks, PSO with support vector machines and a single PSO algorithm. It analyzes the advantages and disadvantages of each algorithm, and presents the development of application of the particle swarm algorithm...
A classification method of video data using centroid neural network is proposed in this paper. The CNN algorithm is used for clustering the MPEG video data. In comparison with other conventional algorithms, The CNN requires neither a predetermined schedule for learning gain nor the total number of iterations for clustering. It always converges to sub-optimal solutions while conventional algorithms...
We propose a novel algorithm based on clustering to extract rules from artificial neural networks. After networks Beijing trained and pruned successfully, inner-rules are generated by discrete activation values of hidden units. Then, weights between input and hidden units are clustered to decrease the complexity of rules extraction. In clustering phase, the clustered number of weights can be adjusted...
Cluster analysis is one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature...
The methods for variables selections which allows for optimal representation of objects group in the data are studied. Neural network based Boolean factor analysis is compared with two statistical methods - linear factor analysis and hierarchical clustering. It is shown that Boolean factor analysis is the best solution for binary data dimensionality reduction. Advantage of Boolean factor analysis...
Adaptive Resonance Theory (ART) and k-means have been widely used for clustering, but those two algorithms have their own limitations. In this paper a hybrid clustering algorithm is proposed which is based on ART2 and k-means. Firstly ATR2 is executed to find the initial cluster numbers and initial cluster centers, k-means uses these values to initialize its parameters and find new cluster centers,...
DNA splice site adjacent sequences have remarkable conservative feature, and mining their underlying biological knowledge has become a key issue in the field of DNA sequences analysis. In this paper, we analyze the feature of human beingpsilas DNA splice site adjacent sequences. Firstly, we propose a kind of DNA splice site sequences clustering method based on Genetic K-modes; secondly, we analyze...
In this paper, constructive neural networks (i.e. CNN) are used to cluster large-scale patterns, and the optimum granularity is chosen by quotient space granularity analysis method. This method not only makes good use of the characteristic of CNN in reducing the computing complexity, but also takes the advantage of quotient space theory in choosing the optimum granularity. So it can cluster large-scale...
Recently quite much attention was given to the investigation of Particle Swarm Optimization algorithm (PSO). It was proved that PSO algorithm has exhibited good performance across wide range application problems. This paper proposes the use of PSO algorithm for decision making model updating. The decision making model is used to generate one-step forward investment decisions for stock markets. The...
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