Behavior-based Outlier Detection for Indoor Environment
- Authors
- Kang, S.; Kim, S.K.
- Issue Date
- 2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- behavior analysis; outlier detection; trajectory clustering
- Citation
- Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, pp.734 - 735
- Journal Title
- Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
- Start Page
- 734
- End Page
- 735
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/31000
- DOI
- 10.1109/CSCI51800.2020.00135
- ISSN
- 0000-0000
- Abstract
- In this paper, we introduce a system that can detect the space outlier utilization of residents in indoor environment at low cost. Our system facilitates autonomous data collection from mobile app logs and the Google app server and generates a high-dimensional dataset required to detect outlier behaviors. For this, we used density-based clustering algorithm with t-distributed stochastic neighbor embedding (t-SNE). Our system enables easy acquisition of large volumes of data required for outlier detection. Our methodology can assist spatial analysis for indoor environments housing residents and help reduce the cost of space utilization feedback. © 2020 IEEE.
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