Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73607821" target="_blank" >RIV/61989592:15310/21:73607821 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-74251-5_29" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-74251-5_29</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-74251-5_29" target="_blank" >10.1007/978-3-030-74251-5_29</a>
Alternative languages
Result language
angličtina
Original language name
Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules
Original language description
Boolean matrix factorization (BMF) is a well-established data analytical method whose goal is to decompose a single large matrix into two, preferably smaller, matrices, carrying the same or similar information as the original matrix. In essence, it can be used to reduce data dimensionality and to provide fundamental insight into data. Existing algorithms are often negatively affected by the presence of noise in the data, which is a common case for real-world datasets. We present an initial study on an algorithm for approximate BMF that uses association rules in a novel way to identify possible noise. This allows us to suppress the impact of noise and improve the quality of results. Moreover, we show that association rules provide a suitable framework allowing the handling of noise in BMF in a justified way.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Data Analysis XIX
ISBN
978-3-030-74250-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
365-375
Publisher name
Springer
Place of publication
Cham
Event location
online
Event date
Apr 26, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722625800029