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The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer's risk. In this paper, four types of abnormal data detection method is introduced, present a new method- the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal...
Accounting for the characteristics of E-commerce Website personal service and the features of users' and goods' similarities distribution, an E-commerce recommendation method based on clustering using genetic algorithm is designed. By using a composite weight matrix to integrate the situation of users purchasing, this method improves the result of clustering, and the result of clustering reflects...
From the view of granularity, this paper presents a genetic clustering algorithm based on dynamic granularity. In view of a parallel, random search, global optimization and diversity characteristics of genetic algorithm, it is combined with dynamic granularity model. In the process of granularity changing, appropriate granulation can be made by coarsening and refining the granularity, which can ensure...
Genetic Algorithms (GA) is a method based on natural selection in the theory of biological evolution, which has been widely applied to solve numerous optimization problems in diverse fields. However, the canonical GA is more likely to get stuck at a local optimum and thereby leads to premature convergence. To overcome such inconvenience, a fuzzy adaptive GA (FAGA) is proposed based on fuzzy clustering...
To improve the accuracy of clustering classification, the Chaos Genetic Algorithm was proposed. In this algorithm, the ergodic property of chaos phenomenon is used to optimize the initial population, so it can accelerate the convergence of Genetic Algorithms. Chaotic systems are sensitive to initial condition system parameters. In order to escape from local optimums, the chaos operator was applied...
Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this...
In the last few decades, evolutionary algorithms (EAs) for solving optimization problems have come to the forefront. Because of the complexity of the problem, Multi-objective problems (MOPs) as well as global optimization problem has been developed so far, but parents for genetic reproduction has been considered as one global group in general. In this paper, we apply clustering algorithm to differential...
This paper applies the artificial fish swarm algorithm (AFSA) to fuzzy clustering. An improved AFSA with adaptive visual and adaptive step is proposed. AFSA enhances the performance of the fuzzy C-means (FCM) algorithm. A computational experiment shows that AFSA improved FCM out performs both the conventional FCM algorithm and the genetic algorithm (GA) improved FCM.
To achieve braking control of locomotive brake control system (LBCS) accurately and steadily under high nonlinearity various time delay condition, a locomotive brake control method based on T-S fuzzy modeling predictive control (MPC) is proposed. Firstly, the paper uses fuzzy clustering method (FCM) to initial parameters, and uses back-propagation algorithm to rectify rectified its premise parameters...
Searching is an important procedure in optimization problems. As is an effective clustering method especially in spatial data mining, the role of searching is essential. While many searching methods focus themselves on particle swarm optimization and genetic algorithms, we propose a new searching algorithm based differential evolution (DE). It proves that DE is a simple optimization algorithm effective...
Optimizing the system performance metric directly is an important method for correcting wave-front distortions in adaptive optics(AO) systems. Appropriate stochastic parallel optimization control algorithm is the key to correcting distorted wave front successfully. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 32-element deformable mirror was...
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