Paper Info
Title
Mining Several Kinds of Temporal Association Rules Enhanced by Tree Structures
Abstract
Market basket analysis is one important application of knowledge discovery in databases. Real life market basket databases usually contain temporal coherences, which cannot be captured by means of standard association rule mining. Thus there is a need for developing algorithms, that reveal such temporal coherences within this data. This paper gathers several notions of temporal association rules and presents an approach for mining most of these kinds (cyclic, lifespan- and calendar-based) in a market basket database, enhanced by two novel tree structures. We called these two tree structures EP- and ET-Tree, which are derived from existing approaches improving standard association rule mining. They are used as representation of the database and thus make the discovery of temporal association rules very efficient.
Year
DOI
Venue
2010
10.1109/eKNOW.2010.16
eKNOW
Keywords
Field
DocType
tree structures ep,temporal association rules,novel tree structure,tree structures,knowledge discovery,real life market basket,important application,market basket database,market basket analysis,temporal coherence,temporal association rule,standard association rule mining,association rule,tree data structures,association rule mining,algorithm design and analysis,data mining,association rules,database management systems,tree structure
Data mining,Algorithm design,Market basket,Computer science,Apriori algorithm,Tree (data structure),Association rule learning,Knowledge extraction,Tree structure,Affinity analysis
Conference
Citations 
PageRank 
References 
2
0.38
12
Authors
2
Name
Order
Citations
PageRank
Tim Schlüter1172.55
Stefan Conrad2168105.91