In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 ...In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.展开更多
Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are mat...Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.展开更多
Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime valu...Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime value(CLV) is presented to evaluate customers in terms of recency,frequency and monetary(RFM) variables.A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging(OWA) and K-Means cluster algorithm.OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty.K-Means algorithm is used to cluster customers according to RFM values.Churn customers could be found out by comparing RFM values of every cluster group with average RFM.Questionnaire is conducted to investigate which reasons cause customers dissatisfaction.Rank these reasons to help E-commerce improve services.The experimental results have demonstrated that the model is effective and reasonable.展开更多
基金Scientific Research Program Funded by Shaanxi Provincial Education Department,China(No.2013JK0749)
文摘In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.
基金supported by the National Key Basic Research Program of China (973 Program) under Grant No. 2011CB302605the National High Technical Research and Development Program of China (863 Program) underGrants No. 2010AA012504,No. 2011AA010705+1 种基金the National Natural Science Foundation of China under Grant No. 60903166the National Science and Technology Support Program under Grants No. 2012BAH37B00,No. 2012-BAH37B01
文摘Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.
基金supported by the Natural Science Foundation under Grant Nos.71273139,60804047the Social Science Foundation of Chinese Ministry of Education under Grant No.12YJC630271
文摘Customers are of great importance to E-commerce in intense competition.It is known that twenty percent customers produce eighty percent profiles.Thus,how to find these customers is very critical.Customer lifetime value(CLV) is presented to evaluate customers in terms of recency,frequency and monetary(RFM) variables.A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging(OWA) and K-Means cluster algorithm.OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty.K-Means algorithm is used to cluster customers according to RFM values.Churn customers could be found out by comparing RFM values of every cluster group with average RFM.Questionnaire is conducted to investigate which reasons cause customers dissatisfaction.Rank these reasons to help E-commerce improve services.The experimental results have demonstrated that the model is effective and reasonable.
