Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
WLPMiner: weighted frequent pattern mining with length-decreasing support constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
On pushing weight constraints deeply into frequent itemset mining
Intelligent Data Analysis
Sequential pattern mining algorithm for automotive warranty data
Computers and Industrial Engineering
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Discovering forward sequences from temporal data
Knowledge-Based Systems
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
A data mining approach to discovering reliable sequential patterns
Journal of Systems and Software
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Sequential pattern mining is an essential research topic with broad applications which discovers the set of frequent subsequences satisfying a support threshold in a sequence database. The major problems of mining sequential patterns are that a huge set of sequential patterns are generated and the computation time is so high. Although efficient algorithms have been developed to tackle these problems, the performance of the algorithms dramatically degrades in case of mining long sequential patterns in dense databases or using low minimum supports. In addition, the algorithms may reduce the number of patterns but unimportant patterns are still found in the result patterns. It would be better if the unimportant patterns could be pruned first, resulting in fewer but important patterns after mining. In this paper, we suggest a new framework for mining weighted frequent patterns in which weight constraints are deeply pushed in sequential pattern mining. Previous sequential mining algorithms treat sequential patterns uniformly while real sequential patterns have different importance. In our approach, the weights of items are given according to the priority or importance. During the mining process, we consider not only supports but also weights of patterns. Based on the framework, we present a weighted sequential pattern mining algorithm (WSpan). To our knowledge, this is the first work to mine weighted sequential patterns. The experimental results show that WSpan detects fewer but important weighted sequential patterns in large sequence databases even with a low minimum threshold.