Machine learning methods for generating high dimensional discrete datasets

Autor: Manco, Giuseppe, Ritacco, Ettore, Rullo, Antonino, Saccà, Domenico, Serra, Edoardo

Publikováno v: WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 12 (2022). doi:10.1002/widm.1450
info:cnr-pdr/source/autori:Manco, Giuseppe; Ritacco, Ettore; Rullo, Antonino; Sacca, Domenico; Serra, Edoardo/titolo:Machine learning methods for generating high dimensional discrete datasets/doi:10.1002%2Fwidm.1450/rivista:WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume:12

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step app

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d82fb615c0117bbac3e074b3ac57632
http://www.cnr.it/prodotto/i/465111

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Decomposition technique for the inverse frequent itemset mining problem

Autor: Guzzo, A., Moccia, L., Domenico Sacca', Serra, E.

Publikováno v: Scopus-Elsevier
19th Italian Symposium on Advanced Database Systems, SEBD 2011, pp. 66–75, 26/06/2011
info:cnr-pdr/source/autori:Guzzo, Antonella; Moccia, Luigi; Saccà, D.; Serra, Edoardo/congresso_nome:19th Italian Symposium on Advanced Database Systems, SEBD 2011/congresso_luogo:/congresso_data:26%2F06%2F2011/anno:2011/pagina_da:66/pagina_a:75/intervallo_pagine:66–75

The Inverse Frequent itemset Mining (IFM) is the problem of computing a transaction database D satisfying specified support constraints on a given set S of itemsets, that are typically the frequent ones. Earlier studies focused on investigating compu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1f28364a3cffe0669d6008d0fade2041
http://www.scopus.com/inward/record.url?eid=2-s2.0-84873684560&partnerID=MN8TOARS

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