Feature selection by principle component analysis for mining frequent association rules

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099081" target="_blank" >RIV/61989100:27240/16:86099081 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_9" target="_blank" >http://dx.doi.org/10.1007/978-3-319-33609-1_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_9" target="_blank" >10.1007/978-3-319-33609-1_9</a>

Result language
angličtina
Original language name
Feature selection by principle component analysis for mining frequent association rules
Original language description
Data mining techniques have been increasingly studied. Extracting the association rules have been the focus of this studies. Recently research have focused on association rules to help uncover relationships between seemingly unrelated data in a relational database or other information repository. The large size of data makes the extraction of association rules hard task. In this paper, we propose a new method for dimension reduction and feature selection based on the Principal Component Analysis, then find the association rules by using the FP-Growth Algorithm. Experimental results reveals that the reduction technique can discover the same rules obtained by the original data. (C) Springer International Publishing Switzerland 2016.
Czech name
—
Czech description
—

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing. Volume 450
ISBN
978-3-319-33608-4
ISSN
1615-3871
e-ISSN
—
Number of pages
11
Pages from-to
99-109
Publisher name
Springer
Place of publication
Berlin
Event location
Soči
Event date
May 16, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)