Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted freque...Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.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
文摘Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.
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.
