Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Data Analysis XIX

ISBN

978-3-030-74250-8

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

365-375

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

online

Datum konání akce

26. 4. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000722625800029