dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns

In: Computing and Informatics, vol. 26, no. 3

J. Blaťák - L. Popelínský

Detaily:

Rok, strany: 2007, 345 - 366

Kľúčové slová:

frequent patterns, inductive logic programming, parallel and distributed data mining, propositionalization

O článku:

In this paper we present dRAP-Independent, an algorithm for independent distributed mining of first-order frequent patterns. This system is based on RAP, an algorithm for finding maximal frequent patterns in first-order logic. dRAP-Independent 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.

Ako citovať:

ISO 690:

Blaťák, J., Popelínský, L. 2007. dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns. In Computing and Informatics, vol. 26, no.3, pp. 345-366. 1335-9150.

APA:

Blaťák, J., Popelínský, L. (2007). dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns. Computing and Informatics, 26(3), 345-366. 1335-9150.