Reassessing Taxonomy-Based Data Clustering

Unveiling Insights and Guidelines for Application

authored by: M. Heumann, T. Kraschewski, O. Werth, M.H. Breitner
Abstract: Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.
Organisation(s): Institute of Computer Science for Business Administration
External Organisation(s): OFFIS - Institute for Information Technology
Type: Article
Journal: Decision Support Systems
Volume: 187
ISSN: 0167-9236
Publication date: 12.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Information Systems and Management, Information Systems, Developmental and Educational Psychology, Arts and Humanities (miscellaneous), Management Information Systems
Electronic version(s): https://doi.org/10.1016/j.dss.2024.114344 (Access: Open)

BibTeX

@article{324f0555113247b7ba522677e94cd6f3,
title = "Reassessing Taxonomy-Based Data Clustering: Unveiling Insights and Guidelines for Application",
abstract = "Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.",
keywords = "Archetypes, Categorical data, Clustering, Guidelines, Taxonomies",
author = "M. Heumann and T. Kraschewski and O. Werth and M.H. Breitner",
year = "2024",
month = dec,
doi = "10.1016/j.dss.2024.114344",
language = "English",
volume = "187",
journal = "Decision Support Systems",
issn = "0167-9236",
publisher = "Elsevier",
}