Abstract
The aim of this paper is to describe the cluster analysis of the failure data from the industrial process. The failure data used in the research were obtained from the automotive industry. The purpose of this analysis is to look at the data in broader view, and to discover various relationships in the data considering different parameters using data mining technique. The data analysis was performed by using hierarchical clustering for finding relationships between failures. We chose the hierarchical clustering analysis to find previously unknown relationships between given failure types, which is the type of task cluster analysis is mostly used for.
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Acknowledgments
This publication is the result of implementation of the project VEGA 1/0673/15: “Knowledge discovery for hierarchical control of technological and production processes” supported by the VEGA.
This publication is the result of implementation of the project: “UNIVERSITY SCIENTIFIC PARK: CAMPUS MTF STU - CAMBO” (ITMS: 26220220179) supported by the Research & Development Operational Program funded by the EFRR.
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Nemeth, M., Michalconok, G. (2017). Finding Relationships in Industrial Data with the Use of Hierarchical Clustering. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_24
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DOI: https://doi.org/10.1007/978-3-319-57141-6_24
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