Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
Fuzzy set technology in knowledge discovery
Fuzzy Sets and Systems
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
OpenMP on networks of workstations
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
TAR: Temporal Association Rules on Evolving Numerical Attributes
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
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Framework for Temporal Data Mining
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A fuzzy data mining algorithm for finding sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IEEE Transactions on Knowledge and Data Engineering
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Looking into the seeds of time: Discovering temporal patterns in large transaction sets
Information Sciences: an International Journal
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Normalised support: a virtual angle of measurement of 'interestingness'
International Journal of Data Analysis Techniques and Strategies
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This paper presents an algorithm for mining fuzzy temporal patterns from a given process instance. The fuzzy representation of time intervals embedded between the activities is used for this purpose. Initially, the activities are portrayed with their temporal relationships through temporal graphs and then, the defined data structures are used to retrieve the data suitable for the proposed algorithm. Similar to the familiar k-itemsets and k-dim sequences, their counterparts are introduced in this work. The proposed process-instance level data structure generates an optimum number of temporal itemsets. The proposed algorithm differs from the other existing algorithms on this topic in the representation of the mined data and patterns. An example is provided to demonstrate the algorithm.