2007
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
BLAŤÁK, Jan a Lubomír POPELÍNSKÝZákladní údaje
Originální název
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Název česky
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Název anglicky
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Autoři
BLAŤÁK, Jan (203 Česká republika) a Lubomír POPELÍNSKÝ (203 Česká republika, garant)
Vydání
Computing and Informatics, Bratislava, 2007, 1335-9150
Další údaje
Jazyk
čeština
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Impakt faktor
Impact factor: 0.349
Kód RIV
RIV/00216224:14330/07:00023860
Organizační jednotka
Fakulta informatiky
UT WoS
000247846400008
Klíčová slova anglicky
data mining; inductive logic programming; frequent patterns; distributed data mining
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 10. 2. 2008 16:55, doc. RNDr. Lubomír Popelínský, Ph.D.
V originále
In this paper we present drapi, an algorithm for independent distributed mining of first-order frequent pattern. This system is based on RAP, an algorithm for finding maximal frequent patterns in first-order logic. drapi utilizes a modified data partitioning schema introduced by Savasere et al. and offers good performance and low communication overhead. We analyze the performance of the algorithm on four different tasks: Mutagenicity prediction - a standard ILP benchmark, information extraction from biological texts, context-sensitive spelling correction, and morphological disambiguation of Czech. The results of the analysis show that the algorithm can generate more patterns than the serial algorithm RAP in the same overall time.
Anglicky
In this paper we present drapi, an algorithm for independent distributed mining of first-order frequent pattern. This system is based on RAP, an algorithm for finding maximal frequent patterns in first-order logic. drapi utilizes a modified data partitioning schema introduced by Savasere et al. and offers good performance and low communication overhead. We analyze the performance of the algorithm on four different tasks: Mutagenicity prediction - a standard ILP benchmark, information extraction from biological texts, context-sensitive spelling correction, and morphological disambiguation of Czech. The results of the analysis show that the algorithm can generate more patterns than the serial algorithm RAP in the same overall time.
Návaznosti
| MSM0021622418, záměr |
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