Knowledge discovery in databases: an overview
AI Magazine
Mining web logs to improve website organization
Proceedings of the 10th international conference on World Wide Web
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
Journal of Systems and Software
Integrating information retrieval and data mining to discover project team coordination patterns
Decision Support Systems
Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
SQUIRE: Sequential pattern mining with quantities
Journal of Systems and Software
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Web usage mining with intentional browsing data
Expert Systems with Applications: An International Journal
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis
Expert Systems with Applications: An International Journal
Knowledge discovery on RFM model using Bernoulli sequence
Expert Systems with Applications: An International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Electronic Commerce Research and Applications
The Cyclic Model Analysis on Sequential Patterns
IEEE Transactions on Knowledge and Data Engineering
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
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In today's business environment, there is tremendous interest in the mining of interesting patterns for superior decision making. Although many successful customer relationship management (CRM) applications have been developed based on sequential pattern mining techniques, they basically assume that the importance of each customer is the same. Previous studies in CRM show that not all customers make the same contribution to a business, and it is indispensible to evaluate customer value before developing effective marketing strategies. Therefore, this study includes the concepts of recency, frequency, and monetary (RFM) analysis in the sequential pattern mining process. For a given subsequence, each customer sequence contributes its own recency, frequency, and monetary scores to represent customer importance. An efficient algorithm is developed to discover sequential patterns with high recency, frequency, and monetary scores. Empirical results show that the proposed method is efficient and can effectively discover more valuable patterns than conventional frequent pattern mining.