Multi-view Clustering Analyses for District Heating Substations

Shahrooz Abghari, Veselka Boeva, Jens Brage, Håkan Grahn

2020

Abstract

In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.

Paper Citation

in Harvard Style

Abghari S., Boeva V., Brage J. and Grahn H. (2020). Multi-view Clustering Analyses for District Heating Substations.In Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-440-4, pages 158-168. DOI: 10.5220/0009780001580168

in Bibtex Style

@conference{data20,
author={Shahrooz Abghari and Veselka Boeva and Jens Brage and Håkan Grahn},
title={Multi-view Clustering Analyses for District Heating Substations},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2020},
pages={158-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009780001580168},
isbn={978-989-758-440-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Multi-view Clustering Analyses for District Heating Substations
SN - 978-989-758-440-4
AU - Abghari S.
AU - Boeva V.
AU - Brage J.
AU - Grahn H.
PY - 2020
SP - 158
EP - 168
DO - 10.5220/0009780001580168