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Occupancy schedules learning process through a data mining framework
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“Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [193,882] to deep neural networks [896] that take input from occupancy sensors [193], WiFi signals [896,897], or appliance power consumption data [882].”
From Tackling climate change with machine learning (2023) · cites this paper as [193]
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,996
Hand, D. J., Mannila, H., & Smyth, P. (2001) · Cited by 2,581
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
From data mining to knowledge discovery: an overview
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Knowledge Discovery and Data Mining · Cited by 2,395
Fast discovery of association rules
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. T. T., & Verkamo, A. I. (1996) · Knowledge Discovery and Data Mining · Cited by 2,376
Smith, A. E. (2002) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(02)00058-1 · Cited by 1,711
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,413
Flagged first, then by citations
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- RapidMiner Studio, V 5.3, Cambridge, MA 02138, USA 〈http://rapid-i.com/content/view/181/190/〉.
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Metadata
- Authors
- D’Oca, S., & Hong, T.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Hong, Tianzhen
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 88
- Pages
- 395-408
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W1981885501
- Cited by
- 294
- References
- 33
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 17 · 2023: 26 · 2022: 27 · 2021: 33 · 2020: 32 · 2019: 25 · 2018: 36 · 2017: 31 · 2016: 26 · 2015: 12
- FWCI
- 31.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Energy and Buildings (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-AC02-05CH11231)
- U.S.-China Clean Energy Research Center
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- building occupancy, occupancy schedules, office buildings, occupancy patterns, building energy simulation, decision tree, rule induction, clustering, occupant behavior, human-building interaction
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