Using frequent closed itemsets for data dimensionality reduction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F11%3A33119207" target="_blank" >RIV/61989592:15310/11:33119207 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using frequent closed itemsets for data dimensionality reduction
Original language description
We address important issues of dimensionality reduction of transactional data sets where the input data consists of lists of transactions, each of them being a finite set of items. The reduction consists in finding a small set of new items, so-called factor-items, which is considerably smaller than the original set of items while comprising full or nearly full information about the original items. Using this type of reduction, the original data set can be represented by a smaller transactional data setusing factor-items instead of the original items, thus reducing its dimensionality. The procedure utilized in this paper is based on approximate Boolean matrix decomposition. In this paper, we focus on the role of frequent closed itemsets that can be used to determine factor-items. We present the factorization problem, its reduction to Boolean matrix decompositions, experiments with publicly available data sets, and an algorithm for computing decompositions.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP202%2F10%2F0262" target="_blank" >GAP202/10/0262: Decompositions of matrices with binary and ordinal data: theory, algorithms, and complexity</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the ICDM 2011, The 11th IEEE International Conference on Data Mining
ISBN
978-0-7695-4408-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1128-1133
Publisher name
IEEE Computer Society, Conference Publishing Services, Los Alamitos, California
Place of publication
Los Alamitos, California
Event location
Vancouver, Canada
Event date
Dec 11, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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