A novel approach for mining closed clickstream patterns
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63544038" target="_blank" >RIV/70883521:28140/21:63544038 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/01969722.2020.1871225" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/01969722.2020.1871225</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/01969722.2020.1871225" target="_blank" >10.1080/01969722.2020.1871225</a>
Alternative languages
Result language
angličtina
Original language name
A novel approach for mining closed clickstream patterns
Original language description
Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases. © 2021 Taylor & Francis Group, LLC.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cybernetics and Systems
ISSN
0196-9722
e-ISSN
—
Volume of the periodical
52
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
328-349
UT code for WoS article
000606927900001
EID of the result in the Scopus database
2-s2.0-85099369902