Applying Data Mining in Prediction and Classification of Urban Traffic

Autor:	Hamed Ahmadi, Sedigheh Khajouei Nejad, Farid Seifi, Nima Seifi
Rok vydání:	2009
Předmět:	Training set Computer science business.industry Data stream mining Decision tree Machine learning computer.software_genre Field (computer science) Traffic prediction Statistical classification Construction industry Classification methods Data mining Artificial intelligence business computer
Zdroj:	CSIE (3)
DOI:	10.1109/csie.2009.906
Popis:	Data mining is a branch of computer science which recently has a great use for enterprises. Applying data mining methods, huge databases have been analyzed and processed. Data mining techniques are usually hired to mine knowledge and models from enormous data sets for prediction of new events. Furthermore these techniques are commonly used in fields which generate great amount of data that can not be processed by ordinary methods. During the last decade traffic management became a new field of science which produced unlimited data, and this amount of data needed new methods to be processed. It is clear that one of the most important fields in traffic management is Traffic prediction. As a result data mining methods were chosen to generate dependable patterns. In this paper we applied Classification methods to learn traffic behavior and prediction of new events.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::77f60e14288c39bb508b1424b15f615a https://doi.org/10.1109/csie.2009.906 Zobrazit plný text záznamu