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With the rapid development of online shopping, electronic commerce has offered a new channel for instant on-line shopping. It is necessary for company to on-line one-to-one market to e-shoppers. Therefore, the ability to predict e-shoppers' purchase behavior basing on data mining has become a key source of competitive advantage for company. Frequently occurring sequential patterns, identified in sequences...
With the rapid development of online shopping, the ability to intelligently collect and analyze information about E-shoppers has become a key source of competitive advantage for firms. This paper presents an optimal algorithm of modeling dynamic architecture for artificial neural networks (ANN) and a novel machine-learning algorithm for extracting rules from databases via using genetic algorithm....
In the Internet shopping environment, changes of customer's needs grow increasingly outstanding. For discovering the changes, the paper mines the transaction databases of different time periods by using association rule discovery, and extracts the association rules and discovers the changes in network customer behavior by comparison and analysis between the two sets of association rules. This paper...
Credit risk evaluation decisions are important for the e-business due to the high level of risk associated with wrong decisions. The process of making credit risk evaluation decision is complex and unstructured. Markov chain is known to perform reasonably well compared to alternate methods for this problem. In this work, we describe this method and the case of a successful application of it to e-business...
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