Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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 Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Efficient mining of generalized association rules with non-uniform minimum support
Data & Knowledge Engineering
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
A genetic-fuzzy mining approach for items with multiple minimum supports
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
Decision Support and Business Intelligence Systems
Decision Support and Business Intelligence Systems
Analysis on repeat-buying patterns
Knowledge-Based Systems
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
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Sequential pattern mining (SPM) is an important technique for determining time-related behavior in sequence databases. In real-life applications, the frequencies for various items in a sequence database are not exactly equal. If all items are set with the same minimum support, the rare item problem may result, meaning that we are unable to effectively retrieve interesting patterns regardless of whether minsup is set too high or too low. Liu (2006) first included the concept of multiple minimum supports (MMSs) to SPM. It allows users to specify the minimum item support (MIS) for each item according to its natural frequency. A generalized sequential pattern-based algorithm, named Multiple Supports - Generalized Sequential Pattern (MS-GSP), was also developed to mine complete set of sequential patterns. However, the MS-GSP adopts candidate generate-and-test approach, which has been recognized as a costly and time-consuming method in pattern discovery. For the efficient mining of sequential patterns with MMSs, this study first proposes a compact data structure, called a Preorder Linked Multiple Supports tree (PLMS-tree), to store and compress the entire sequence database. Based on a PLMS-tree, we develop an efficient algorithm, Multiple Supports - Conditional Pattern growth (MSCP-growth), to discover the complete set of patterns. The experimental result shows that the proposed approach achieves more preferable findings than the MS-GSP and the conventional SPM.