Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
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
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Linear Time Algorithms for Finding Maximal Forward References
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth 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
Frequent Episode Rules for Internet Anomaly Detection
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Efficient algorithms for incremental Web log mining with dynamic thresholds
The VLDB Journal — The International Journal on Very Large Data Bases
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Mining Frequent Purchase Behavior Patterns for Commercial Websites
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Mining frequent episodes for relating financial events and stock trends
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Analysis on repeat-buying patterns
Knowledge-Based Systems
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
Estimating sequential bias in online reviews: A Kalman filtering approach
Knowledge-Based Systems
Mining significant usage patterns from clickstream data
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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Traditionally, sequence pattern mining has been used to mine items occurs in time sequences and items were deemed to be irrelevant to each other. However, in real applications, sequence items shown in a record may have some relation. For example, in mining students' learning portfolios, the learning progressions must contain learning objects with forward-directed relations. Namely, the learning objects (items) themselves are evidence of a pre-existing relationship. In addition, most sequence mining algorithms assume the sequence records in databases are all of the same age. Each data record is observed at the same starting and ending point. But, according to the occurrences of events in a given period, the lengths of some sequences are longer than others. Hence the sequences with longer time spans might contain longer patterns than those with shorter time spans. As a result, the frequency of possible patterns shown in longer sequences might be underestimated due to fewer of records. This research proposed two methods, FSP and FSP-LC, to analyze forward sequence data. Latter, a real-life database which records all employee progression histories in a large company was used to verify and explain the proposed methods. The experimental results show that the proposed methods can mine specific and longer sequences to further improve and re-design their personnel systems.