Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining high-speed data streams
Proceedings of the sixth 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
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
webSPADE: A Parallel Sequence Mining Algorithm to Analyze Web Log Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on 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
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Information Systems - Databases: Creation, management and utilization
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs
Proceedings of the 6th annual ACM international workshop on Web information and data management
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences
Data Mining and Knowledge Discovery
Mining block correlations to improve storage performance
ACM Transactions on Storage (TOS)
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Parallel mining of closed sequential patterns
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
CP-Miner: Finding Copy-Paste and Related Bugs in Large-Scale Software Code
IEEE Transactions on Software Engineering
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining compressed sequential patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A sequential pattern mining algorithm using rough set theory
International Journal of Approximate Reasoning
Efficient incremental mining of frequent sequence generators
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
MSGPs: a novel algorithm for mining sequential generator patterns
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
A new approach for problem of sequential pattern mining
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Recent study shows that mining compact frequent patterns (such as closed patterns and compressed patterns) can alleviate the interpretability and efficiency problem encountered by traditional frequent pattern mining methods. Compact frequent patterns keep exact or approximate supports of a complete set of frequent patterns, and the number of them is often orders of magnitude smaller. Several efficient algorithms have been proposed to mine compact sequential patterns. However, sequence databases are not always static. Sequences (or items) are often added to and deleted from databases. A slight change made on a database may lead to the change of compact patterns. Mining from scratch is very time-consuming and thus infeasible. In this paper, we explore how to efficiently maintain closed sequential patterns in a dynamic sequence database environment. A compact structure CSTree is designed to keep closed sequential patterns, and its nice properties are carefully studied. Two efficient algorithms, IMCS"A and IMCS"D, are developed to maintain the CSTree upon incremental update. The algorithms make full use of the properties of CSTree to find nodes whose states are obsolete and avoid unnecessary node extension and closure checking operations to accelerate the incremental update process. A thorough experimental study on various real and synthetic datasets shows that the proposed algorithms outperform the state-of-the-art algorithms - PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.