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In the process of codebook design of vector quantization, traditional LBG algorithm owns the advantage of fast convergence, but it is prone to local optimum and is influenced greatly by initial codebook. Given that the Genetic Algorithm has the capability to produce global optimal results, this paper proposes a new clustering algorithm GA-L based on GA and LBG to improve the quality of codebook. This...
With the wide application of GIS to all kinds of fields, and developing of the technique of data mining and spatial data collection, the technique of data mining in spatial database-spatial data mining is coming out. In order to satisfy the people's demand for the interesting and potentially useful knowledge from the spatial database, this thesis used a wide using spatial clustering algorithm: k-means...
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...
As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading...
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...
This paper proposes an improved genetic algorithm, it keeps the population diversity by similarity checks on the population before selection, and the algorithm solves the early-maturing problem of the population evolution, and proposes a formula for mutation probability related with similarity rate and iteration times. The algorithm not only maintains a good diversity of population, but also guarantees...
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...
Fuzzy K-prototypes is a very efficient algorithm for processing large scale mixed data set, but the selection of initial clustering center has an important impact on the clustering effect of algorithm. FKP algorithm is improved by using genetic algorithm in this paper. Seeking the initial clustering center for fuzzy K-prototypes algorithm by using genetic algorithm overcomes the shortcoming effectively,...
In traditional Gene Expression Programming (GEP), individuals' survival too much depends on fitness while their relationships are ignored. Borrowing the idea from the minority protection in real life, this study introduces a novel Cluster Delegate algorithm (CDA) and makes the following contributions: (1) propose several new concepts including individual similarity, ??- cluster, and the farthest neighborhood...
By researching all kinds of methods for document clustering, we put forward a new dynamic method based on genetic algorithm (GA). K-means is a greedy algorithm, which is sensitive to the choice of cluster center and very easily results in local optimization. Genetic algorithm is a global convergence algorithm, which can find the best cluster centers easily. Among the traditional document clustering...
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...
The shortcomings about present genetic algorithm applying to classification are analyzed. Using the method of minimum propagating tree can cluster complex shape and non-overlap sample candidate solutions into races. The algorithm regulates optimization with "race" method and controls individuals in a micro way with race crossover. We also mixed crossover operator based on the thought of...
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.
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional genetic algorithm and the modification of mutation operations improve the...
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...
Distributed clustering is a new research field of data mining now. In this paper, one of distributed clustering named DCBKC (distributed clustering based on K-means and coarse-grained parallel genetic algorithm) based on K-means and coarse-grained parallel genetic algorithm is advanced. The algorithm can solve local clustering problem of distributed clustering effectively, reflect all of local data...
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