Adaptive Clustering for Dynamic IoT Data Streams
- Submitting institution
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The University of Surrey
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9012178_2
- Type
- D - Journal article
- DOI
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10.1109/JIOT.2016.2618909
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- 64
- Volume
- 4
- Issue
- 1
- ISSN
- 2327-4662
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Conventional clustering methods for large scale time-series data can be quickly over-fitted and are not effective in dealing with multivariate real-world data. Adaptive and effective clustering of real-world time-series can lead to the development of automated methods for grouping and analysing patterns and activity detection. This paper describes a new machine learning algorithm that is able to adapt to data and concept drifts in processing real world sensory measurements. The proposed algorithm outperforms all the current state-of-the-art adaptive methods in clustering streaming IoT data and offers a unique solution to autonomously adapt to dynamic changes in the data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -