Paper Info
Title
Efficient temporal pattern mining for humanoid robot
Abstract
Pattern mining in temporal databases is one of the challenging platform which holds attention when some ordered sequences are frequently occurred at different time instances in the dataset. We have found temporal patterns in humanoid robot dataset of HOAP-2 (Humanoid Open-Architecture Platform) which generates different motions through recurring sequences of various joint associations. For mining temporal patterns in that dataset we have proposed a method. This method uses FP-Temporal and SH(Soft-Hyperlinked)-Temporal mining algorithm as pattern growth methods for generating temporal association rules for various motion patterns of HOAP-2. Brief performance analysis shows that SH-Temporal is much efficient than FP-Temporal for such datasets and works significantly for mining sequentially associative temporal patterns in terms of temporal association rules.
Year
DOI
Venue
2010
10.1145/1858378.1858397
A2CWiC
Keywords
Field
DocType
efficient temporal pattern mining,different motion,humanoid robot dataset,temporal mining algorithm,different time instance,pattern growth method,pattern mining,temporal association rule,sequentially associative temporal pattern,temporal pattern,temporal databases,humanoid robot,open architecture,temporal database,association rule
Data mining,Associative property,Computer science,Temporal database,Temporal pattern mining,Association rule learning,Data mining algorithm,Humanoid robot
Conference
Citations 
PageRank 
References 
0
0.34
15
Authors
2
Name
Order
Citations
PageRank
Upasna Singh110.70
G. C. Nandi27110.28