Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the ...
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Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing particle swarm and k-meansalgorithm for customer classification, this paper advances a new customer classification algorithm through improving the existing particle swarm algorithm and combining it with k-meansalgorithm. First the paper designs 21 customer classification indicators based on consumer characteristics and behaviors analysis, including customer characteristics type variables and customer behaviors type variables; Second, limitation of particle swarm algorithm and k-meansalgorithm are analyzed; Then corresponding improvements for particle swarm algorithm are advanced including improvement of the speed update formula of particle and , improvement of balancing the development and detection capability of particle of the algorithm; Thirdly, the online trading customer classification algorithm combining the improved particle swarm algorithm and k-meansalgorithm is advanced. Finally the experimental results verify that the new algorithm can improve effectiveness and validity of customer classification when used for classifying network trading customers practically.
Factors that affect crude oil output are multifarious and non-linear, so it is very difficult to analyze and predict the crude oil output solely based on mathematical methods. This paper presents a new method that app...
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ISBN:
(纸本)9781479972845
Factors that affect crude oil output are multifarious and non-linear, so it is very difficult to analyze and predict the crude oil output solely based on mathematical methods. This paper presents a new method that applies TB-SCM algorithm to predict crude oil output. Firstly, the monthly production data of the past years from a sample oil plant is preprocessed by the k-meansalgorithm, and the transaction dataset is obtained. Next, based on the TB-SCM algorithm, the strong association rules about crude oil output are generated with the given minimum support threshold and minimum confidence threshold. Lastly, these strong association rules can help us to forecast crude oil output in the coming months for oil production plant. Comparing with the actual value of crude oil output, the result shows that the prediction method is of high operational efficiency, simple and accurate.
In this paper an off-line Arabic/Farsi handwritten recognition algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi...
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In this paper an off-line Arabic/Farsi handwritten recognition algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi common names. The features are wavelet coefficients extracted from smoothed word image profiles in four standard directions. The Centers of competitive layer of RBF neural network have been determined by combining GA and k_means clustering algorithm. Weights of supervised layer has been trained by using LMS rule and the distances of feature vector of each sample to the centre of RBF network have been computed based on warping function. Experimental results show advantages of this method in field of handwriting recognition.
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