Granular-rule extraction to simplify data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F15%3A50003822" target="_blank" >RIV/62690094:18450/15:50003822 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-15705-4_41" target="_blank" >http://dx.doi.org/10.1007/978-3-319-15705-4_41</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-15705-4_41" target="_blank" >10.1007/978-3-319-15705-4_41</a>

Result language
angličtina
Original language name
Granular-rule extraction to simplify data
Original language description
Granulation simplifies the data to better understand its complexity. It comforts this understanding by extracting the structure of data, essentially in big data or cloud computing scales. It can extract a simple granular-rules set from a complex data set. Granulation is associated with theory of fuzzy information granulation, which can be supported by fuzzy C-mean clustering. However, intersections of fuzzy clusters create redundant granular-rules. This paper proposes a granular-rules extraction methodto simplify a data set into a granular-rule set with unique granular-rules. It performs based on two stages to construct and prune the granular-rules. We use four data sets to reveal the results, i.e., wine, servo, iris, and concrete compressive strength. The results reveal the ability of proposed method to simplify data sets by 58% to 91%.
Czech name
—
Czech description
—

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Asian conference onIntelligent information and database systems (ACIIDS 2015)
ISBN
978-3-319-15704-7
ISSN
0302-9743
e-ISSN
—
Number of pages
9
Pages from-to
421-429
Publisher name
Springer
Place of publication
Berlin
Event location
Bali, Indonésie
Event date
Mar 23, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)