Efficient algorithms for mining colossal patterns in high dimensional databases

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238741" target="_blank" >RIV/61989100:27240/17:10238741 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S095070511730045X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S095070511730045X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.knosys.2017.01.034" target="_blank" >10.1016/j.knosys.2017.01.034</a>

Result language

angličtina

Original language name

Efficient algorithms for mining colossal patterns in high dimensional databases

Original language description

Mining association rules plays an important role in decision support systems. To mine strong association rules, it is necessary to mine frequent patterns. There are many algorithms that have been developed to efficiently mine frequent patterns, such as Apriori, Eclat, FP-Growth, PrePost, and FIN. However, these are only efficient with a small number of items in the database. When a database has a large number of items (from thousands to hundreds of thousands) but the number of transactions is small, these algorithms cannot run when the minimum support threshold is also small (because the search space is huge). This thus causes the problem of mining colossal patterns in high dimensional databases. In 2012, Sohrabi and Barforoush proposed the BVBUC algorithm for training colossal patterns based on a bottom up scheme. However, this needs more time to check subsets and supersets, because it generates a lot of candidates and consumes more memory to store these. In this paper we propose new, efficient algorithms for mining colossal patterns. Firstly, the CP (Colossal Pattern)-tree is designed. Next, we develop two theorems to rapidly compute patterns of nodes and prune nodes without the loss of information in colossal patterns. Based on the CP-tree and these theorems, an algorithm (named CP-Miner) is proposed to solve the problem of mining colossal patterns. A Sorting strategy for efficiently mining colossal patterns is thus developed. This strategy helps to reduce the number of significant candidates and the time needed to check subsets and supersets. The PCP-Miner algorithm, which Uses this strategy, is then proposed, and we also conduct experiments to show the efficiency of these algorithms. (C) 2017 Elsevier B.V. All rights reserved.

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

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

—

Volume of the periodical

122

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

15

Pages from-to

75-89

UT code for WoS article

000395604800007

EID of the result in the Scopus database

2-s2.0-85011271399