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2022 | OriginalPaper | Chapter

15. Semantic-MDBScan: An Approach to Assign a Semantic Interpretation to Behavior Changes Detected in Data Stream Scenarios

Authors : Eldane Vieira Júnior, Rita Maria Silva Julia, Elaine Ribeiro Faria

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter presents Semantic-MDBScan, an innovative algorithm that extends M-DBScan to assign semantic meaning to behavior changes detected in data streams. It addresses the limitations of traditional batch and adaptive learning techniques in dynamic environments, focusing on non-stationary data distributions and concept drift. The algorithm uses an unsupervised change detection technique based on entropy and Markov Chain, combined with classification methods to provide semantic interpretation. The experiments validate the algorithm's effectiveness in both abrupt and gradual change scenarios, demonstrating its potential for building intelligent agents capable of adapting to real-world changes.
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Metadata
Title
Semantic-MDBScan: An Approach to Assign a Semantic Interpretation to Behavior Changes Detected in Data Stream Scenarios
Authors
Eldane Vieira Júnior
Rita Maria Silva Julia
Elaine Ribeiro Faria
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_15

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