Data structures and algorithm analysis in C (2nd ed.)
Data structures and algorithm analysis in C (2nd ed.)
Mining features for sequence classification
KDD '99 Proceedings of the fifth 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
Prediction of Web Page Accesses by Proxy Server Log
World Wide Web
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
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
FlExPat: Flexible Extraction of Sequential Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
Journal of Parallel and Distributed Computing - 19th International parallel and distributed processing symposium
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Analyzing sequential patterns in retail databases
Journal of Computer Science and Technology
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Contiguous item sequential pattern mining using UpDown Tree
Intelligent Data Analysis
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Effective database transformation and efficient support computation for mining sequential patterns
Journal of Intelligent Information Systems
Efficient frequent sequence mining by a dynamic strategy switching algorithm
The VLDB Journal — The International Journal on Very Large Data Bases
Mining sequential patterns across multiple sequence databases
Data & Knowledge Engineering
Mining multidimensional and multilevel sequential patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
The MineSP operator for mining sequential patterns in inductive databases
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Parallel mining of maximal sequential patterns using multiple samples
The Journal of Supercomputing
Fast discovery of time-constrained sequential patterns using time-indexes
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Effective database transformation and efficient support computation for mining sequential patterns
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
A pruning technique to discover correlated sequential patterns in retail databases
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Mining sequential support affinity patterns with weight constraints
ICDCIT'06 Proceedings of the Third international conference on Distributed Computing and Internet Technology
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
Healthcare trajectory mining by combining multidimensional component and itemsets
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Mining sequential patterns in large databases is animportant research topic. The main challenge of miningsequential patterns is the high processing cost due to thelarge amount of data. In this paper, we propose a newstrategy called DIrect Sequence Comparison (abbreviatedas DISC), which can find frequent sequences without havingto compute the support counts of non-frequent sequences.The main difference between the DISC strategy and theprevious works is the way to prune non-frequent sequences.The previous works are based on the anti-monotoneproperty, which prune the non-frequent sequencesaccording to the frequent sequences with shorter lengths.On the contrary, the DISC strategy prunes the non-frequentsequences according to the other sequences with the samelength. Moreover, we summarize three strategies used in theprevious works and design an efficient algorithm calledDISC-all to take advantages of all the four strategies. Theexperimental results show that the DISC-all algorithmoutperforms the PrefixSpan algorithm on mining frequentsequences in large databases. In addition, we analyze thesestrategies to design the dynamic version of our algorithm,which achieves a much better performance.