Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/29001
Autoria: | Antonio, N. de Almeida, A. Nunes, L. |
Editor: | Zheng Xiang Matthias Fuchs Ulrike Gretzel Wolfram Höpken |
Data: | 2022 |
Título próprio: | Data mining and predictive analytics for E-tourism |
Título e volume do livro: | Handbook of e-Tourism |
Referência bibliográfica: | Antonio, N., de Almeida, A., & Nunes, L. (2022). Data mining and predictive analytics for E-tourism. Em Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken (Eds.). Handbook of e-Tourism (pp.1-25). Springer. https://doi.org/10.1007/978-3-030-05324-6_29-1 |
ISBN: | 978-3-030-05324-6 |
DOI (Digital Object Identifier): | 10.1007/978-3-030-05324-6_29-1 |
Palavras-chave: | Database mining Data visualization Knowledge discovery Machine learning Predictive analytics Predictive modeling |
Resumo: | Computers and devices, today ubiquitous in our daily life, foster the generation of vast amounts of data. Turning data into information and knowledge is the core of data mining and predictive analytics. Data mining uses machine learning, statistics, data visualization, databases, and other computer science methods to find patterns in data and extract knowledge from information. While data mining is usually associated with causal-explanatory statistical modeling, predictive analytics is associated with empirical prediction modeling, including the assessment of the quality of the prediction. This chapter intends to offer the readers, even those unfamiliar with this topic, a general overview of the key concepts and potential applications of data mining and predictive analytics and to help them to successfully apply e-tourism concepts in their research projects. As such, the chapter presents the fundamentals and common definitions of/in data mining and predictive analytics, including the types of problems to which it can be applied and the most common methods and techniques employed. The chapter also explains what is known as the life cycle of data mining and predictive analytics projects, describing the tasks that compose the most widely employed process model, both for industry and academia: the Cross-Industry Standard Process for Data Mining, CRISP-DM. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-CLI - Capítulos de livros internacionais IT-CLI - Capítulos de livros internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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bookPart_85252.pdf | 985,3 kB | Adobe PDF | Ver/Abrir |
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