| 1. |
- Abghari, Shahrooz, et al.
(författare)
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Higher order mining for monitoring district heating substations
- 2019
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Ingår i: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728144931 ; , s. 382-391
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Konferensbidrag (refereegranskat)abstract
- We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques.
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| 2. |
- Johansson, E., et al.
(författare)
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Crime Hotspots : An Evaluation of the KDE Spatial Mapping Technique
- 2015
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Ingår i: Proceedings - 2015 European Intelligence and Security Informatics Conference, EISIC 2015. - : IEEE Computer Society. - 9781479986576 ; , s. 69-74
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Konferensbidrag (refereegranskat)abstract
- Residential burglaries are increasing. By visualizing patterns as spatial hotspots, law-enforcement agents can get a better understanding of crime distributions and trends. Two aspects are investigated, first, measuring the accuracy and performance of the KDE algorithm using small data sets. Secondly, investigation of the amount of crime data needed to compute accurate and reliable hotspots. The Prediction Accuracy Index is used to effectively measure the accuracy of the algorithm. The data from three geographical areas in Sweden, including Stockholm, Gothenburg and Malmö are analyzed and evaluated over a one year. The results suggest that the usage of the KDE algorithm to predict residential burglaries performs well overall when having access to enough crimes, but is capable with small data sets as well
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