Discovering periodic itemsets using novel periodicity measures

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/19:10242190

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Discovering periodic itemsets using novel periodicity measures

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Discovering periodic itemsets using novel periodicity measures

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LM2015070" target="_blank" >LM2015070: IT4Innovations národní superpočítačové centrum</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Advances in Electrical and Electronic Engineering

ISSN

1336-1376

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

12

Strana od-do

33-44

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85067039429