@InProceedings{Supelec537,
author = {Frederic Pennerath and Amedeo Napoli},
title = {{The Model of Most Informative Patterns and its Application to Knowledge Extraction from Graph Databases}},
year = {2009},
booktitle = {{Machine Learning and Knowledge Discovery in Databases, European Conference (ECML PKDD 2009), Proceedings, Part II}},
publisher = {Springer},
volume = {5782},
pages = {205-220},
month = {September},
editor = {Wray L. Buntine and Marko Grobelnik and Dunja Mladenic and John Shawe-Taylor},
series = {Lecture Notes in Computer Science},
address = {Bled (Slovenia)},
url = {http://dx.doi.org/10.1007/978-3-642-04174-7_14},
isbn = {978-3-642-04173-0},
doi = {10.1007/978-3-642-04174-7_14},
abstract = {This article introduces the class of Most Informative Patterns
(MIPs) for characterizing a given dataset. MIPs form a reduced
subset of non redundant closed patterns that are extracted from
data thanks to a scoring function depending on domain knowledge.
Accordingly, MIPs are designed for providing experts good
insights on the content of datasets during data analysis. The
article presents the model of MIPs and their formal properties
wrt other kinds of patterns. Then, two algorithms for extracting
MIPs are detailed: the first directly searches for MIPs in a
dataset while the second screens MIPs from frequent patterns.
The
efficiencies of both algorithms are compared when applied to
reference datasets. Finally the application of MIPs to labelled
graphs, here molecular graphs, is discussed.}
}