Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
A general framework for time granularity and its application to temporal reasoning
Annals of Mathematics and Artificial Intelligence
Temporal Semantic Assumptions and Their Use in Databases
IEEE Transactions on Knowledge and Data Engineering
Implementing Calendars and Temporal Rules in Next Generation Databases
Proceedings of the Tenth International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Distribution Discovery: Local Analysis of Temporal Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering Local Patterns from Multiple Temporal Sequences
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
A review on time series data mining
Engineering Applications of Artificial Intelligence
Spatio–temporal rule mining: issues and techniques
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A tree structure for event-based sequence mining
Knowledge-Based Systems
Hi-index | 0.00 |
Many events repeat themselves as the time goes by. For example, an institute pays its employees on the first day of every month. However, events may not repeat with a constant span of time. In the payday example here, the span of time between each two consecutive paydays ranges between 28 and 31 days. As a result, regularity, or temporal pattern, has to be captured with a use of granularities (such as day, week, month, and year), oftentimes multiple granularities. This paper defines the above patterns, and proposes a number of pattern discovery algorithms. To focus on the basics, the paper assumes that a list of events with their timestamps is given, and the algorithms try to find patterns for the events. All of the algorithms repeat two possibly interleaving steps, with the first step generating possible (called candidate) patterns, and the second step verifying if candidate patterns satisfy some user-given requirements. The algorithms use pruning techniques to reduce the number of candidate patterns, and adopt a data structure to efficiently implement the second step. Experiments show that the pruning techniques and the data structure are quite effective.