An efficient method for mining frequent sequential patterns using multi-Core processors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238742" target="_blank" >RIV/61989100:27240/17:10238742 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007%2Fs10489-016-0859-y" target="_blank" >https://link.springer.com/article/10.1007%2Fs10489-016-0859-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-016-0859-y" target="_blank" >10.1007/s10489-016-0859-y</a>

Result language

angličtina

Original language name

An efficient method for mining frequent sequential patterns using multi-Core processors

Original language description

The problem of mining frequent sequential patterns (FSPs) has attracted a great deal of research attention. Although there are many efficient algorithms for mining FSPs, the mining time is still high, especially for large or dense datasets. Parallel processing has been widely applied to improve processing speed for various problems. Some parallel algorithms have been proposed, but most of them have problems related to synchronization and load balancing. Based on a multi-core processor architecture, this paper proposes a load-balancing parallel approach called Parallel Dynamic Bit Vector Sequential Pattern Mining (pDBV-SPM) for mining FSPs from huge datasets using the dynamic bit vector data structure for fast determining support values. In the pDBV-SPM approach, the support count is sorted in ascending order before the set of frequent 1-sequences is partitioned into parts, each of which is assigned to a task on a processor so that most of the nodes in the leftmost branches will be infrequent and thus pruned during the search; this strategy helps to better balance the search tree. Experiments are conducted to verify the effectiveness of pDBV-SPM. The experimental results show that the proposed algorithm outperforms PIB-PRISM for mining FSPs in terms of mining time and memory usage.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Intelligence

ISSN

0924-669X

e-ISSN

—

Volume of the periodical

46

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

703-7016

UT code for WoS article

000398110300014

EID of the result in the Scopus database

2-s2.0-84994378107