Temporal knowledge discovery with infrequent episodes

Authors:
Dan Li;Liying Jiang;Jitender S. Deogun
Affiliations:
University of Nebraska - Lincoln, Lincoln, NE;University of Nebraska - Lincoln, Lincoln, NE;University of Nebraska - Lincoln, Lincoln, NE
Venue:
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Year:
2004

Citing 5
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery

Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th 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
Efficient rule discovery in a geo-spatial decision support system

dg.o '02 Proceedings of the 2002 annual national conference on Digital government research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an efficient algorithm which discovers rare episodes with a combination of bottom-up and top-down scanning schema. The information sharing between bottom-up and top-down scannings helps prune candidate episodes, and thus, efficiently find infrequent episodes that are interesting to user: We evaluate the performance of the algorithm using real-life weather databases. We observe from experimental results that our approach results in 30%-90% reduction in computation time and 25%-75% reduction in the number of candidates comparing with Apriori algorithm.