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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery

verfasst von : Ning Cao, Yansong Wang, Xiaoyu Chen, Yulan Zhou, Mingrui Wu, Xiaofang Li, Jianrui Ding, Dongjie Zhu

Erschienen in: Advances in Artificial Intelligence and Security

Verlag: Springer International Publishing

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Abstract

In recent years, the emergence of a large number of venture firms has brought great profits to venture capital firms. However, it is not easy to identify venture firms with investment prospects. Therefore, based on frequent item sets, this paper mainly mines the enterprise text information to identify the venture enterprises with investment prospects. Firstly, we use TF-IDF algorithm to extract keywords from enterprise text; Secondly, the word2VEC model is used to vectorize the text keywords, and cosine similarity is calculated with the word vectors in the keyword database; Finally, we use the Apriori algorithm to find frequent item sets and generate association rules, complete vector weighting calculation of combination keywords, and finally retain the first three words or phrases with the highest weight as the identification keywords of the enterprise, thus determining whether the enterprise is a risk company with potential investment prospects. Experimental results show that the proposed method is effective.

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Vorheriges Kapitel Anomaly Detection Based on Isolated Forests
Nächstes Kapitel Design of Abnormal Behavior Detection System in the State Grid Business Office
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Metadaten
Titel
An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery
verfasst von
Ning Cao
Yansong Wang
Xiaoyu Chen
Yulan Zhou
Mingrui Wu
Xiaofang Li
Jianrui Ding
Dongjie Zhu
Copyright-Jahr
2021
Buch
Advances in Artificial Intelligence and Security

Print ISBN: 978-3-030-78617-5

Electronic ISBN: 978-3-030-78618-2

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-78618-2_41

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