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@article{562245,
author = {Blaťák, Jan and Popelínský, Lubomír},
article_location = {Praha},
article_number = {5},
keywords = {knowledge discovery in databases; inductive logic programming; frequent patterns; feature construction; propositionalization},
language = {eng},
issn = {1210-0552},
journal = {Neural Network World},
title = {Mining first-order maximal frequent patterns},
volume = {14},
year = {2004}
}
TY - JOUR
ID - 562245
AU - Blaťák, Jan - Popelínský, Lubomír
PY - 2004
TI - Mining first-order maximal frequent patterns
JF - Neural Network World
VL - 14
IS - 5
SP - 381-390
EP - 381-390
PB - UIVT AV ČR
SN - 12100552
KW - knowledge discovery in databases
KW - inductive logic programming
KW - frequent patterns
KW - feature construction
KW - propositionalization
N2 - Frequent patterns discovery is one of the most important data mining tasks. We introduce RAP, the first system for finding first-order maximal frequent patterns. We describe search strategies and methods of pruning the search space. RAP generates long patterns much faster than other systems.RAP has been used for feature construction for propositional as well as multirelational data. We prove that partial search for maximal frequent patterns as new features is competitive with other approaches and results in classification accuracy increase.
ER -
BLAŤÁK, Jan a Lubomír POPELÍNSKÝ. Mining first-order maximal frequent patterns. \textit{Neural Network World}. Praha: UIVT AV ČR, roč.~14, č.~5, s.~381-390. ISSN~1210-0552. 2004.
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