PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric
Journal of the ACM (JACM)
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
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
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
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
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Improving Index Compression Using Cluster Information
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A two stage approach for contiguous sequential pattern mining
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
| Hi-index | 0.00 |
In this paper the problem of Contiguous Item Sequential Pattern (CISP) Mining is presented as a sequential pattern mining problem under two constraints. First, each element in a sequence consists of only one item. Second, items appearing in the sequences that contain a pattern must be adjacent with respect to the underlying order as they appear in the pattern. Even though the problem of CISP mining can be solved by using previous approaches on sequential pattern mining under a general constraint description framework, this may lead to poor performance due to the large searching space. To efficiently solve this problem, a new data structure, UpDown Tree, is proposed for CISP mining. UpDown Tree based approach can greatly improve the efficiency of CISP mining in terms of both time and memory comparing to previous approaches. An extensive experimental study has shown promising results with our approach.