Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Elements of the Theory of Computation
Elements of the Theory of Computation
Efficient Data Mining for Path Traversal Patterns
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
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh 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
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Perfect hashing schemes for mining traversal patterns
Fundamenta Informaticae
Computational aspects of mining maximal frequent patterns
Theoretical Computer Science
Mining for weak periodic signals in time series databases
Intelligent Data Analysis
Efficient mining of frequent episodes from complex sequences
Information Systems
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SIMPLE FUZZY GRID PARTITION FOR MINING MULTIPLE-LEVEL FUZZY SEQUENTIAL PATTERNS
Cybernetics and Systems
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
Efficient constraint evaluation in categorical sequential pattern mining for trajectory databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Efficient frequent sequence mining by a dynamic strategy switching algorithm
The VLDB Journal — The International Journal on Very Large Data Bases
A lower bound on the sample size needed to perform a significant frequent pattern mining task
Pattern Recognition Letters
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Attribute Constrained Rules for Partially Labeled Sequence Completion
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Bayesian approaches to ranking sequential patterns interestingness
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Mining patterns of dyspepsia symptoms across time points using constraint association rules
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Sequential pattern mining in multi-relational datasets
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Improving constrained pattern mining with first-fail-based heuristics
Data Mining and Knowledge Discovery
A sequential pattern mining algorithm using rough set theory
International Journal of Approximate Reasoning
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained sequential pattern knowledge in multi-relational learning
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Implicit enumeration of patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
COBRA: closed sequential pattern mining using bi-phase reduction approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Mining frequent trees with node-inclusion constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Data Mining and Knowledge Discovery
Predictive sequence miner in ILP learning
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Perfect Hashing Schemes for Mining Traversal Patterns
Fundamenta Informaticae
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
Intelligent Data Analysis
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Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT驴Sequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like antimonotonicity) are integrated into the mining process. A quantitative exploration of these trade-offs is conducted through an extensive experimental study on synthetic and real-life data sets. The experimental results clearly validate the effectiveness of our approach, showing that speedups of more than an order of magnitude are possible when RE constraints are pushed deep inside the mining process. Our experimentation with real-life data also illustrates the versatility of REs as a user-level tool for focusing on interesting patterns.