Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63524899" target="_blank" >RIV/70883521:28140/20:63524899 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0167739X19314475" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167739X19314475</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2020.01.034" target="_blank" >10.1016/j.future.2020.01.034</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Popis výsledku v původním jazyce

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.

Název v anglickém jazyce

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Popis výsledku anglicky

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

FUTURE GENERATION COMPUTER SYSTEMS

ISSN

0167-739X

e-ISSN

—

Svazek periodika

107

Číslo periodika v rámci svazku

Neuvedeno

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

18-30

Kód UT WoS článku

000527331800002

EID výsledku v databázi Scopus

2-s2.0-85078857727