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001-es BibID:	BIBFORM049044
Első szerző:	Kádek Tamás (programtervező matematikus)
Cím:	Extended breadth-first search algorithm / Kádek Tamás, Pánovics János
Dátum:	2013
ISSN:	1694-0814 (Print) 1694-0784 (Online)
Megjegyzések:	The task of artificial intelligence is to provide representation techniques for describing problems, as well as search algorithms that can be used to answer our questions. A widespread and elaborated model is state-space representation, which, however, has some shortcomings. Classical search algorithms are not applicable in practice when the state space contains even only a few tens of thousands of states. We can give remedy to this problem by defining some kind of heuristic knowledge. In case of classical state-space representation, heuristic must be defined so that it qualifies an arbitrary state based on its "goodness," which is obviously not trivial. In our paper, we introduce an algorithm that gives us the ability to handle huge state spaces and to use a heuristic concept which is easier to embed into search algorithms.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban Artificial intelligence State-space representation Extended model Breadth-first search Intelligens város közösségi alkotásból
Megjelenés:	International Journal of Computer Science Issues 10 : 6 (2013), p. 78-82. -
További szerzők:	Pánovics János (1975-) (programtervező matematikus)
Pályázati támogatás:	TÁMOP-4.2.2.C-11/1/KONV-2012-0001 TÁMOP Adat menedzsment és tudásfeltárás intelligens város alkalmazásokhoz
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM012418
Első szerző:	Mahdi Esmaeili (informatikus)
Cím:	Finding Sequential Patterns from Large Sequence Data / Mahdi Esmaeil, Fazekas Gabor
Dátum:	2010
ISSN:	1694-0814 (Print) 1694-0784 (Online)
Megjegyzések:	Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks. Some of the common ones are classification, clustering, association rule mining, and sequential pattern mining. Sequential pattern mining finds sets of data items that occur together frequently in some sequences. Sequential pattern mining, which extracts frequent subsequences from a sequence database, has attracted a great deal of interest during the recent surge in data mining research because it is the basis of many applications, such as: web user analysis, stock trend prediction, DNA sequence analysis, finding language or linguistic patterns from natural language texts, and using the history of symptoms to predict certain kind of disease. The diversity of the applications may not be possible to apply a single sequential pattern model to all these problems. Each application may require a unique model and solution. A number of research projects were established in recent years to develop meaningful sequential pattern models and efficient algorithms for mining these patterns. In this paper, we theoretically provided a brief overview three types of sequential patterns model and some properties of them.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban Sequential Pattern Mining Periodic Pattern Approximate Pattern Data Mining
Megjelenés:	International Journal of Computer Science Issues. - 1 : 1 (2010), p. 43-46. -
További szerzők:	Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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