Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Alternative Interest Measures for Mining Associations in Databases
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
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Evaluation of Interestingness Measures for Ranking Discovered Knowledge
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Incremental Meta-Mining from Large Temporal Data Sets
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining maximal frequent intervals
Proceedings of the 2003 ACM symposium on Applied computing
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Data Mining and Knowledge Discovery
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering interesting patterns through user's interactive feedback
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Mining minimal distinguishing subsequence patterns with gap constraints
Knowledge and Information Systems
Discovering Frequent Poly-Regions in DNA Sequences
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Data & Knowledge Engineering
Generalizing the notion of confidence
Knowledge and Information Systems
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
IEEE Transactions on Knowledge and Data Engineering
GO-SPADE: mining sequential patterns over datasets with consecutive repetitions
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Mining bridging rules between conceptual clusters
Applied Intelligence
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Activity recognition with finite state machines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
International Journal of Data Mining and Bioinformatics
Finding representative objects using link analysis ranking
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
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The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.