@inproceedings{Schmidt.2004j,
  abstract = {Based on the Levenshtein distance, a method of sequence analysis to compare and classify variations in human-computer interaction behavior is presented. If interaction patterns can be described as a characterizing series of discrete events, this method can be used to measure the similarity between two interaction behavior strings of different length. Using quadratic, symmetric distance matrices the influence of sequence length on dissimilarity can be calculated separately. Besides the sum of squared distances from pair wise sequence comparisons as a ranking criterion, a hierarchical cluster analysis gives enhanced possibilities of further exploration and characterizing classes of procedures that were executed by the users. Together with the description of the method, its application is illustrated utilizing a software system for product development as an example of use.},
  added-at = {2024-09-17T10:38:37.000+0200},
  address = {Galway},
  author = {Schmidt, L. and Luczak, H.},
  biburl = {https://www.bibsonomy.org/bibtex/2a8d1bb4a7f63ed383280cc2f39c72a15/sdt},
  booktitle = {Human and Organisational Issues in the Digital Enterprise: Proceedings of the 9th International Conference on Human Aspects of Advanced Manufacturing: Agility and Hybrid Automation (Galway 2004)},
  editor = {Fallon, E. F. and Karwowski, W.},
  interhash = {47a4004f6e96411debbe46b09f432b6d},
  intrahash = {a8d1bb4a7f63ed383280cc2f39c72a15},
  keywords = {Algorithmus Clusteranalyse HCI Interaktion Levenshtein_distance mmspub},
  pages = {547–556},
  publisher = {{The Department of Industrial Engineering, National University of Ireland Galway}},
  timestamp = {2024-09-17T10:38:37.000+0200},
  title = {Comparison and Classification of Human-Computer Interaction Behaviour Using the Levenshtein Distance},
  year = 2004
}