Detailed Information on Publication Record
2007
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
BLAŤÁK, Jan and Lubomír POPELÍNSKÝBasic information
Original name
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Name in Czech
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Name (in English)
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns
Authors
BLAŤÁK, Jan (203 Czech Republic) and Lubomír POPELÍNSKÝ (203 Czech Republic, guarantor)
Edition
Computing and Informatics, Bratislava, 2007, 1335-9150
Other information
Language
Czech
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
Impact factor
Impact factor: 0.349
RIV identification code
RIV/00216224:14330/07:00023860
Organization unit
Faculty of Informatics
UT WoS
000247846400008
Keywords in English
data mining; inductive logic programming; frequent patterns; distributed data mining
Tags
International impact, Reviewed
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.
In English
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.
Links
| MSM0021622418, plan (intention) |
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