Találati lista | Tudóstér

001-es BibID:	BIBFORM092514
Első szerző:	Szathmáry László (programtervező-informatikus)
Cím:	Generating Rare Association Rules Using the Minimal Rare Itemsets Family / László Szathmáry, Petko Valtchev, Amedeo Napoli
Dátum:	2010
ISSN:	1673-7288
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk data mining knowledge discovery in databases (KDD) itemset extraction rara itemsets rare association rules rara item problem
Megjelenés:	International Journal of Software and Informatics. - 4 : 3 (2010), p. 219-238. -
További szerzők:	Valtchev, Petko Napoli, Amedeo
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM087373
Első szerző:	Szathmáry László (programtervező-informatikus)
Cím:	A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes / Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, Robert Godin, Alix Boc, Vladimir Makarenkov
Dátum:	2013
ISSN:	1012-2443 1573-7470
Megjegyzések:	In pattern mining and association rule mining, there is a variety of algorithms for mining frequent closed itemsets (FCIs) and frequent generators (FGs), whereas a smaller part further involves the precedence relation between FCIs. The interplay of these three constructs and their joint computation have been studied within the formal concept analysis (FCA) field yet none of the proposed algorithms is scalable. In frequent pattern mining, at least one suite of efficient algorithms has been designed that exploits basically the same ideas and follows the same overall computational schema. Based on an in-depth analysis of the aforementioned interplay that is rooted in a fundamental duality from hypergraph theory, we propose a new schema that should enable for a more parsimonious computation. We exemplify the new schema in the design of Snow-Touch, a concrete FCI/FG/precedence miner that reuses an existing algorithm, Charm, for mining FCIs, and completes it with two original methods for mining FGs and precedence, respectively. The performance of Snow-Touch and of its closest competitor, Charm-L, were experimentally compared using a large variety of datasets. The outcome of the experimental study suggests that our method outperforms Charm-L on dense data while on sparse one the trend is reversed. Furthermore, we demonstrate the usefulness of our method and the new schema through an application to the analysis of a genome dataset. The initial results reported here confirm the capacity of the method to focus on significant associations.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Annals of Mathematics and Artificial Intelligence. - 70 : 1-2 (2013), p. 81-105. -
További szerzők:	Valtchev, Petko Napoli, Amedeo Godin, Robert Boc, Alix Makarenkov, Vladimir
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: