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This paper focuses on the problem of seabed survey mission with multiple target points distributed in large scale area. To complete the survey with AUVs in minimum cost, a two-step procedure using greed strategy is presented. In the first step, the locations of so-called anchor points are found by executing k-means algorithm iteratively. In the second step, an ant-cycle system is used to find out...
This research investigates the performance of a region-based segmentation, K-Means clustering and Fuzzy C-Means (FCM) for two types of orchid Vanda and Ascocentrum genus. Orchid is the largest family of angiosperms. Among this orchid, Vanda and Ascocentrum are the most famous vandaceous. Some of the orchids have the same color, shape, and appearance. Florist sometimes makes mistakes due to this similarity...
A solution is designed for the vehicles to minimize the cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In Clarke and Wrights saving method and Chopra and Meindl savings matrix method mainly an efficient vehicle routing can be achieved by calculating the distance matrix and savings matrix values based on the...
Coherency group identification is an integral constituent part of the wider field of reduction techniques in power systems. It consists of separating the machines in the system into groups that feature similar behavior. This paper presents a coherency identification algorithm for dynamic studies. The algorithm combines both modal and time domain techniques in an effort to combine the merits of both...
In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis...
Aiming at the disadvantages of the single BP neural network in speech recognition, a method of speech recognition based on k-means clustering and neural network ensembles is presented in this paper. At first, a number of individual neural networks are trained, and then the k-means clustering algorithm is used to select a part of the trained individuals' weights and thresholds for improving diversity...
This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental...
By analyzing the actuality of the data mining in ecommerce environment, and considering the complexity and the curse of dimensionality about extracting the implicit and unknown knowledge brought by the massive and high-dimensional data. Based on the K-means clustering, Particle Swarm Optimization (PSO) clustering and hybrid PSO clustering algorithm, this paper presented a model which combined Principal...
Through research on K-means algorithm of text clustering and semantic-based vector space model, a semantic-based K-means text clustering model is proposed to solve the problem on high-dimensional and sparse characteristics of text data set. The model reduces the semantic loss of the text data and improves the quality of text clustering. Experiments prove that semantic-based text clustering increases...
A supervised multi-model modeling method is proposed for the nonlinear system in this paper. In the traditional k-means clustering method, the error of modeling multi-model is always ignored or even not considered in the clustering process. So, this unsupervised clustering method has large modeling error. In the new modeling method, the initial clusters are firstly obtained by the k-means clustering,...
In high dimensional data space, clusters are likely to exist in different subspaces. K-means is a classic clustering algorithm, but it cannot be used to find subspace clusters. In this paper, an algorithm called GKM is designed to generalize k-means algorithm for high dimensional data. In the objective function of GKM, we associate a weight vector with each cluster to indicate which dimensions are...
Perhaps the most fundamental consideration when modeling data as a mixture of Gaussians is the number of components in the mixture. To this end, numerous approaches have been proposed, ranging from the classic use of statistical hypothesis testing methods to make decisions, to the determination of balance between the model Goodness-of-Fit (GoF) and complexity. In this paper, we explore an existing...
In this paper, we present an approach for a patch-based adaptive mesh refinement (AMR) for multi-physics simulations. The approach consists of clustering, symmetry preserving, mesh continuity, flux correction, communications, management of patches, and dynamical load balance. Among the special features of this patch-based AMR are symmetry preserving, efficiency of refinement, special implementation...
This paper presents a study on area change detection applications based on remote sensing data. The crucial parts of the process are in selecting the optimal combination of bands and in the image clustering process, so that we could obtain the object regions correctly. The proposed methodology consists of the following steps: (i) image band selection using Optimum Index Factor; (ii) K-Means clustering...
One of the most important goals of time series analysis is prediction basing on the analyzed information. But it is not easy to analyze the patterns, regularities and trends of non-stationary and/or chaos time series because their major characteristics are non-linear and vague. In this paper, we propose primary and secondary tuning procedures that can enhance the accuracy for designing fuzzy prediction...
The radial basis function (RBF) is designed and implemented for modelling a nonlinear system. The combination of k-means clustering algorithm, p-nearest neighbour, least mean square together with the Gaussian function are used for determining the optimal network parameters adaptively instead of trial and error. In this paper an algorithm is presented to design (RBF) with smallest possible number of...
To assess quality fast and accurate, analyze the K-means clustering, point out that the main advantages of k-means algorithm are its simplicity and speed which allows it to run on large datasets .Introduce the method of particle swarm optimization, through calculation, point out that all the particles are likely to faster convergence on the optimal solution. According to the character of quality assessment...
The current study presented a generalized regression neural network (GRNN) based approach to predict nitrogen oxides (NOx) emitted from coal-fired boiler. A novel 'multiple' smoothing parameters, which is different from the standard algorithm in which only single smoothing parameter was adopted (Matlab neural network toolbox, for example), were assigned to GRNN model. K-means clustering algorithm...
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