Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Web usage mining for Web site evaluation
Communications of the ACM
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
Scalable data mining for rules
Scalable data mining for rules
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A new algorithm for gap constrained sequence mining
Proceedings of the 2004 ACM symposium on Applied computing
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive sequence discovery by incremental mining
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Mining contiguous sequential patterns from web logs
Proceedings of the 16th international conference on World Wide Web
A General Model for Sequential Pattern Mining with a Progressive Database
IEEE Transactions on Knowledge and Data Engineering
Alarms Association Rules Based on Sequential Pattern Mining Algorithm
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Innovations and Advanced Techniques in Computer and Information Sciences and Engineering
Innovations and Advanced Techniques in Computer and Information Sciences and Engineering
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
An incremental mining algorithm for maintaining sequential patterns using pre-large sequences
Expert Systems with Applications: An International Journal
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
Efficient Mining of Closed Sequential Patterns on Stream Sliding Window
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Efficient mining of frequent items coupled with weight and /or support over progressive databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
| Hi-index | 0.00 |
This paper delineates the purpose of an algorithm for mining constraint sequential patterns from a progressive database. We construct the updated CSSF-trie from the static database with the intention of efficiently capturing the dynamic nature of data addition and deletion into the mining problem. Whenever the database gets updated from the distributed sources, the database may be static, inserted, or deleted. CSSF trie is also updated by including the updated sequence. The updated CSSF-trie is used to mine the progressive CSSF-patterns using the proposed algorithm. Finally, the experimentation is carried out using the synthetic and real life distributed databases that are given to the progressive CSSF-miner using thread environment. The experimental results provide better results in terms of the generated number of sequential patterns, execution time and the memory usage over the existing IncSpan algorithm.