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Multi-view Clustering Analyses for District Heating Substations [Elektronisk resurs]

Abghari, Shahrooz (författare)
Proceedings of the 9th International Conference on Data Science, Technology and Applications (DATA) 
Boeva, Veselka (författare)
Brage, Jens (författare)
Grahn, Håkan (författare)
Blekinge Tekniska Högskola Fakulteten för datavetenskaper (utgivare)
Publicerad: SciTePress, 2020
Engelska.
Ingår i: DATA20. ; 158-168
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  • 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. 

Ämnesord

Natural Sciences  (hsv)
Computer and Information Sciences  (hsv)
Computer Sciences  (hsv)
Naturvetenskap  (hsv)
Data- och informationsvetenskap  (hsv)
Datavetenskap (datalogi)  (hsv)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Data Mining
Multi-view Clustering
Multi-layer Clustering
Time Series
District Heating Substation
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