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Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns

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  • Muhammed-Fatih Kaya

    (University of Hohenheim)

  • Mareike Schoop

    (University of Hohenheim)

Abstract

The systematic processing of unstructured communication data as well as the milestone of pattern recognition in order to determine communication groups in negotiations bears many challenges in Machine Learning. In particular, the so-called curse of dimensionality makes the pattern recognition process demanding and requires further research in the negotiation environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition potential based on high-dimensional negotiation communication data. A research approach is presented to evaluate the application potential of selected methods via a holistic framework including three main evaluation milestones: the determination of optimal number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices are initially pre-processed and afterwards used as underlying databases to investigate the pattern recognition potential of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the respective internal as well as external performances. The overall research results show that certain cluster separations are recommended by internal and external performance measures by means of a holistic evaluation approach, whereas three of the clustering separations are eliminated based on the evaluation results.

Suggested Citation

  • Muhammed-Fatih Kaya & Mareike Schoop, 2022. "Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns," Group Decision and Negotiation, Springer, vol. 31(3), pages 555-589, June.
    • Handle: RePEc:spr:grdene:v:31:y:2022:i:3:d:10.1007_s10726-021-09758-7
    DOI: 10.1007/s10726-021-09758-7
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    References listed on IDEAS

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    1. Tran, Thanh N. & Wehrens, Ron & Buydens, Lutgarde M.C., 2006. "KNN-kernel density-based clustering for high-dimensional multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 513-525, November.
    2. Rudolf Vetschera, 2016. "Concessions Dynamics in Electronic Negotiations: A Cross-Lagged Regression Analysis," Group Decision and Negotiation, Springer, vol. 25(2), pages 245-265, March.
    3. Mareike Schoop, 2021. "Negotiation communication revisited," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 163-176, March.
    4. Wendi L. Adair & Jeanne M. Brett, 2005. "The Negotiation Dance: Time, Culture, and Behavioral Sequences in Negotiation," Organization Science, INFORMS, vol. 16(1), pages 33-51, February.
    5. Mareike Schoop & Marije Amelsvoort & Johannes Gettinger & Michael Koerner & Sabine T. Koeszegi & Per Wijst, 2014. "The Interplay of Communication and Decisions in Electronic Negotiations: Communicative Decisions or Decisive Communication?," Group Decision and Negotiation, Springer, vol. 23(2), pages 167-192, March.
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    Cited by:

    1. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.
    2. Reza Mortazavi & Elham Enayati & Abdolali Basiri, 2024. "Accelerated Sequential Data Clustering," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 245-263, July.

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