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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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