Abstract
Anomaly detection enables the identification of the nature of the outliers to determine if they are errors or new trends that need to be understood and learnt by the model for improved generalisation capability. Multivariate and univariate anomaly detection is used to find an unusual point or pattern in a given set. A substantial number of articles have focused on this research domain; however, they primarily reflect on the impacts and trends of multivariate and univariate time-series data research. However, there is still a lack of bibliometric reports exhibiting the exploration of an in-depth research pattern in multivariate and univariate anomaly detection approaches. This study addresses that gap by analysing the widespread multivariate and univariate anomaly detection activities conducted thus far. This study analysed the Scopus database by using bibliometric analysis in a pool of more than 1385 articles that were published between 2010 and 2022, of which 679 are journal articles, 609 are conference papers, 72 are conference reviews, 13 are book chapters, 8 are reviews, 2 are erratum, and 1 is a book and a short survey. The multivariate and univariate anomaly detection bibliometric analysis was developed using R, an open-source statistical tool, and bibliophily was used to analyse the results. The findings reveal the following: (1) multivariate and univariate anomaly detection in collaboration with machine learning can enhance intrusion detection systems; (2) Researchers are interested in combining multivariate and univariate techniques with machine learning and deep learning classification problems to distinguish normality from abnormality; (3) the most active country in this research domain are the United States, China, France, India, Italy, and Germany; (4) Norway, Sweden, and Taiwan published few articles, however, receive many citations; (5) the United States, China, France, and Italy are the countries that collaborate the most in publishing articles on multivariate and univariate anomaly detection approaches; (6) keyword analysis reveals that researchers are adopting multivariate and univariate approaches to detect anomalous patterns in big data and data mining application domain. Supervised, unsupervised and semi-supervised machine learning algorithms, in collaboration with multivariate and univariate, play a significant role in classifying abnormality from normality.
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Guembe, B., Azeta, A., Misra, S., Garg, L. (2023). Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_29
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