@article {343,
	title = {A new customer classification algorithm for electronic commerce enterprises},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {284 - 288},
	abstract = {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. A new customer classification algorithm for electronic commerce enterprises is advanced based on analyzing customer characteristics and behaviors. First, based on consumer characteristics and behavior analysis, 21 customer classification indicators, including customer characteristics type variables and customer behavior type variables, are designed, Second, aiming at the shortages of the existing BP neural network algorithm of data-mining for customer classification, the immune-genetic algorithm is used to correct BP neural network to speed up the convergence of the model. Finally the experimental results verify that the new algorithm can improve customer classification accuracy and can guarantee the effectiveness and validity of customer classification for electronic commerce enterprises in its engineering application.},
	keywords = {BP Neural Network, Customer classification, Electronic commerce, Immune genetics},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870801373\&partnerID=40\&md5=e0e9cd1f8a408b64aa0f9dc6037ec8e6},
	author = {Li, X.}
}
@article {185,
	title = {A novel graph containment query algorithm on graph databases},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Export Date: 10 July 2014},
	month = {2009},
	pages = {143 - 151},
	abstract = {Nowadays, efficient graph query processing method is coming more and more important along with the structured data accumulating. Given a graph database, a graph containment query retrieves the graphs from the database that are subgraphs of the query graph. In this paper, an algorithm is proposed, which is founded on a CFG (Closed Frequent subgraph) based index, to answer graph containment query. CFG is a set of special frequent subgraph of a graph database. When query processes through this index, it gets back a smaller candidate answer set than other methods which means much less subgraph isomorphism calculation. Both theoretical analysis and experimental evaluation result shows that the proposed method not only efficiently prunes the search branches in the feature index, but also effectively reduces the subgraph isomorphism test between the query graph and the indexed features.},
	keywords = {Frequent subgraphs, Graph databases, Graph indexing, Graph querying},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-73249130115\&partnerID=40\&md5=c53fbe52cb4f2481146df6768914245f},
	author = {Li, X. and Zhang, W. and Li, J.}
}
