.png){kind=icon}
Return
Finding relational redescriptions
.png){kind=icon}
PRE
AI
DOI：10.1007/s10994-013-5402-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline Inductive Logic Programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks.
Keywords:
Redescription mining
Relational query mining
Inductive Logic Programming
Graph mining
Relational data mining
Journal
IF:
2.9
Papers:
2.7K
Citations:
3.4W
Organization
Cited Papers
X-ray scattering by myosin S-1: implications for the steric blocking model of muscle control.png)
Nature
NatureIF0
PREAI
ShareSaveShareSave
ShareSave
ShareSaveShareSaveShareSave
View more