Title | Data Mining |
Creator | |
Date Issued | 2023 |
Source Publication | Flavoromics: An Integrated Approach to Flavor and Sensory Assessment |
ISBN | 9781003816010;9781032210629 |
Author/Editor of Source Publication | Leo Nollet, Matteo Bordiga |
Publication Place | Boca Raton |
Publisher | CRC Press |
Abstract | Flavoromics studies the chemical compound profiles of flavor (taste and aroma) using a data-driven methodology to correlate chemical compound profiles with the sensory properties of foods. Data mining is meant to extract meaningful knowledge from useful but non-evident information hidden within large datasets. It is performed to extract meaningful information from the samples and visualize the results. The learning of principles of common supervised and unsupervised multivariate statistical tools is important to select the right data mining methods. Therefore, this chapter introduces techniques for data mining and data analysis tasks, ranging from classification analysis, regression analysis, correlation analysis, and cluster analysis. For each topic, it covers basic concepts, task formulations, methodologies, and evaluation metrics. |
Language | 英语English |
DOI | 10.1201/9781003268758-7 |
URL | View source |
Scopus ID | 2-s2.0-85180909461 |
Citation statistics | |
Document Type | Book chapter |
Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11596 |
Collection | Faculty of Science and Technology |
Affiliation | Division of Science and Technology,Data Science Program,United International College (UIC),Zhuhai,Guangdong Province,China |
First Author Affilication | Beijing Normal-Hong Kong Baptist University |
Recommended Citation GB/T 7714 | Rui, Meng,Wenmeng, He. Data Mining. Boca Raton: CRC Press, 2023. |
Files in This Item: | There are no files associated with this item. |
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