An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Mining of Event-Oriented Negative Sequential Rules
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Proceedings of the 20th ACM international conference on Information and knowledge management
An efficient GA-Based algorithm for mining negative sequential patterns
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Sequential pattern mining is to discover all frequent sequences from a sequence database and has been an important issue in data mining. A lot of methods have been proposed for mining sequential pattern. However, conventional methods consider only the occurrences of itemsets in a sequence database, and the sequential patterns are referred to as positive sequential patterns. In practice, the absence of a frequent itemset in a sequence may imply significant information. In this paper, we introduce negative sequential pattern concept in which the absence of an itemset in a sequence is also considered. The major difficulties of negative sequential pattern mining are that there may be huge amounts of the candidates of negative sequences and most of them are meaningless. We proposed an algorithm for mining negative sequential patterns (NSPM). Using NSPM, we prune a number of redundant candidates by applying apriori-principle, and extract meaningful negative sequences from a large number of frequent negative sequences using the interestingness measure.