Mining temporal patterns from sequence database of interval-based events

Authors:
Yen-Liang Chen;Shin-Yi Wu
Affiliations:
Department of Information Management, National Central University, Chung-Li, Taiwan, China;Department of Information Management, National Central University, Chung-Li, Taiwan, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

Citing 7
Cited 3

FreeSpan: frequent pattern-projected sequential pattern mining

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining knowledge about temporal intervals

Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences

Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth

Proceedings of the 17th International Conference on Data Engineering
Fast Discovery of Sequential Patterns by Memory Indexing

DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery

Domain ontology driven data mining: a medical case study

Proceedings of the 2007 international workshop on Domain driven data mining
Mining temporal interval relational rules from temporal data

Journal of Systems and Software
An approach to discovering multi-temporal patterns and its application to financial databases

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sequential pattern mining is one of the important techniques of data mining to discover some potential useful knowledge from large databases. However, existing approaches for mining sequential patterns are designed for point-based events. In many applications, the essence of events are interval-based, such as disease suffered, stock price increase or decrease, chatting etc. This paper presents a new algorithm to discover temporal pattern from temporal sequences database consisting of interval-based events.