@inproceedings{TrahaschLauerZibello2018,
  author    = {Stephan Trahasch and Tobias Lauer and Ruth Zibello},
  title     = {A Data Clustering Approach for Automated Optical Inspection of Metal Work Pieces},
  series = {ALLDATA 2018, The Fourth International Conference on Big Data, Small Data, Linked Data and Open Data},
  isbn      = {978-1-61208-631-6},
  issn      = {2519-8386},
  pages     = {64 -- 68},
  year      = {2018},
  abstract  = {This paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for the performance criteria, the single-linkage method has achieved better results compared to other agglomeration methods.},
  language  = {en}
}