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Water resources carrying capacity is a crucial part of regional natural resources carrying capacity, which is a restriction factor in the region tightly short of water resources whether can support harmonious development of population, economy and environment. The major difficulties for Water resources carrying capacity assessment was to determine the weight of each indicator. Illuminated by this,...
In RBF neural network designing, hidden neuron number and parameter influence the performance of network. The paper discusses influences of pruning hidden neurons using different criteria and parameter on speech recognition rate of modified RBF neural network. First we introduce three hidden neuron pruning criteria, then propose a modified RBF neural network, at last recognition results before and...
The particular benefit of cloud computing is the simple scalability of large applications, and many companies have already decided to use the cloud for their infrastructures. An enterprise IT infrastructure often includes a workflow management system. In a cloud, various workflow engines can coexist, each with its specific functional responsibility. A central instance is in charge of distributing...
Effective and meaningful visualization techniques are quite important for multidimensional DNA microarray gene expression data analysis. Elucidating the cluster properties of these multidimensional data are often complex. Patterns, hypotheses on the relationships, and ultimately of the function of the gene can be analyzed and visualized by non-linear reduction of the multidimensional data to a lower...
Clustering is a major tool in data analysis, dividing objects into different groups, based on unsupervised training procedures. Clustering algorithms attempt to group a set of objects into well-defined subgroups, based on some similarity between them. The results of the clustering process may not be confirmed by our knowledge of the data. The self-organizing map (SOM) neural network is an excellent...
Food and nutrition are a key to have good health. They are important for everyone to maintain a healthy diet especially for diabetic patients who have several limitations. Nutrition therapy is a major solution to prevent, manage and control diabetes by managing the nutrition based on the belief that food provides vital medicine and maintains a good health. Typically, diabetic patients need to avoid...
SOM neural network is one of the most commonly used Clustering algorithm in the text clustering. The initial connection weights of SOM neural network will affect the degree of convergence. If the Initial connection weights are not set appropriate, that will cause in a long wandering around the local minimum, accordingly lower the speed of convergence, or even cause local convergence or not convergence...
A collaborative emergency call taking information system in the Czech Republic processes calls from the European 112 emergency number. Large amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a...
In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored...
To improve the forecasting precision and generalization capability of neural network, a novel neural network ensemble method is proposed, in which bagging algorithm is used to generate neural network individuals and root of mean square error is adopted as a rule to measure the similarity between networks.By the affinity propagation clustering algorithm, neural network individuals with high precision...
In this paper, we propose a memetic algorithm (MA) for classifier optimization based on a clustering method that applies the k-means algorithm over a specific derived space. In this space, each classifier or individual is represented by the set of the accuracies of the classifier for each class of the problem. The proposed sensitivity clustering is able to obtain groups of individuals that perform...
A collaborative Emergency call taking information system in the Czech Republic processes calls on the European 112 emergency number. Amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a human,...
This paper studies the combination of multiple classifiers with a prototyped-based supervised clustering algorithm, namely SGNG, for Thai printed character recognition. The proposed classification system consists of two steps. First, the prototypes obtained by the SGNG are firstly used to roughly classify an unknown input positioning around a training dataset. Second, several classifiers, such as...
This study presents a new algorithm which extends an input-output clustering method for determining the centers of an RBF network. The proposed method uses the estimated lipschitz constant of a function as an initial weighting factor for augmenting training samples and apply a batch clustering method for determining augmented centers. Then, it adjusts this weighting factor by applying a gradient descent...
Support vector machines (SVMs) represent a well known technique for data classification. However, the complexity of the training process makes the SVMs unsuitable for classifying large datasets. Examples of existing approaches to this problem are sampling of the input datasets or clustering of similar inputs. On the other hand, the growing neural gas algorithm (GNG) is a robust tool for cluster analysis,...
The clustering is one of popular technique for separating the similar data into the same group.The problem of this technique is "How to find the real number of group in the data?". So, In this paper we try to find the solution that can guess the number of group by automatic. However, this number is very difficult to specify if the data space is in a very high dimension. Here, the problem...
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 comparisons of various learning algorithms were presented and it was shown that most popular neural network topologies (MLP) and most popular training algorithm (EBP) are not giving optimal solution. Instead MLP networks much simpler neural network topologies can be used to produce similar or better results. Instead of popular EBP more advance algorithms such as LM or NBN should be used. They...
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...
Water quality comprehensive evaluation of water supply network was present in this paper. It is on the basis of microcosmic model in water supply network. The water quality indicators, by which the states of water quality are expressed, are selected as the input vector for the comprehensive evaluation model. The self-organizing feature map (SOM) and k-means arithmetic are adopted in the model. Since...
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