Discovering periodic itemsets using novel periodicity measures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242190" target="_blank" >RIV/61989100:27240/19:10242190 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/19:10242190

Result on the web

<a href="http://advances.utc.sk/index.php/AEEE/article/view/3185" target="_blank" >http://advances.utc.sk/index.php/AEEE/article/view/3185</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15598/aeee.v17i1.3185" target="_blank" >10.15598/aeee.v17i1.3185</a>

Result language

angličtina

Original language name

Discovering periodic itemsets using novel periodicity measures

Original language description

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxP er (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxP er constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database. (C) 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/LM2015070" target="_blank" >LM2015070: IT4Innovations National Supercomputing Center</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

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

Name of the periodical

Advances in Electrical and Electronic Engineering

ISSN

1336-1376

e-ISSN

—

Volume of the periodical

17

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

12

Pages from-to

33-44

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85067039429