Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Koukal, Bohumil | |
| dc.contributor.author | Chudán, David | |
| dc.contributor.author | Svátek, Vojtěch | |
| dc.contributor.editor | Steinberger, Josef | |
| dc.contributor.editor | Zíma, Martin | |
| dc.contributor.editor | Fiala, Dalibor | |
| dc.contributor.editor | Dostal, Martin | |
| dc.contributor.editor | Nykl, Michal | |
| dc.date.accessioned | 2017-10-09T08:42:49Z | |
| dc.date.available | 2017-10-09T08:42:49Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | STEINBERGER, Josef ed.; ZÍMA, Martin ed.; FIALA, Dalibor ed.; DOSTAL, Martin ed.; NYKL, Michal ed. Data a znalosti 2017: sborník konference, Plzeň, Hotel Angelo 5. - 6. října 2017. 1. vyd. Plzeň: Západočeská univerzita v Plzni, 2017, s. 46-50. ISBN 978-80-261-0720-0. | cs |
| dc.identifier.isbn | 978-80-261-0720-0 | |
| dc.identifier.uri | https://www.zcu.cz/export/sites/zcu/pracoviste/vyd/online/DataAZnalosti2017.pdf | |
| dc.identifier.uri | http://hdl.handle.net/11025/26333 | |
| dc.format | 5 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | cs | cs |
| dc.publisher | Západočeská univerzita v Plzni | cs |
| dc.rights | © Západočeská univerzita v Plzni | cs |
| dc.subject | OLAP navigace | cs |
| dc.subject | pravidla sdružení GUHA | cs |
| dc.subject | řízené analýzy | cs |
| dc.title | OLAP recommender: supporting navigation in OLAP cubes using association rule mining | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | The OLAP Recommender tool automates the multidimensional data exploration process and recommends potentially interesting views on the data to the user. It integrates two data analytics methods – OLAP visualisation and data mining, the latter being represented by GUHA association rule mining. Algo-rithms implemented in the tool include automated data discretization, setup of dimensions’ commensurability, automatic design of the data mining task based on the data structure, and mapping between the mined association rules and the corresponding OLAP visualisation. The system was tested with real retail data and with EU structural funds data. The experiments indicate that complemen-tary usage of association rule mining and OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques. | en |
| dc.subject.translated | OLAP navigation | en |
| dc.subject.translated | GUHA association rules | en |
| dc.subject.translated | guided analytics | en |
| dc.type.status | Peer-reviewed | en |
| Vyskytuje se v kolekcích: | Data a znalosti 2017 Data a znalosti 2017 | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| Koukal.pdf | Plný text | 358,67 kB | Adobe PDF | Zobrazit/otevřít |
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