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2016 | OriginalPaper | Buchkapitel

Similarity-Based Classification for Big Non-Structured and Semi-Structured Recipe Data

verfasst von : Wei Chen, Xiangyu Zhao

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

In current big data era, there has been an explosive growth of various data. Most of these large volume of data are non-structured or semi-structured (e.g., tweets, weibos or blogs), which are difficult to be managed and organized. Therefore, an effective and efficient classification algorithm for such data is essential and critical. In this article, we focus on a specific kind of non-structured/semi-structured data in our daily life: recipe data. Furthermore, we propose the document model and similarity-based classification algorithm for big non-structured and semi-structured recipe data. By adopting the proposed algorithm and system, we conduct the experimental study on a real-world dataset. The results of experiment study verify the effectiveness of the proposed approach and framework.

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Vorheriges Kapitel SBTM: Topic Modeling over Short Texts
Nächstes Kapitel An Approach of Fuzzy Relation Equation and Fuzzy-Rough Set for Multi-label Emotion Intensity Analysis
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Metadaten
Titel
Similarity-Based Classification for Big Non-Structured and Semi-Structured Recipe Data
verfasst von
Wei Chen
Xiangyu Zhao
Copyright-Jahr
2016
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-32054-0

Electronic ISBN: 978-3-319-32055-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-32055-7_5

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