Iglesias, F., Zseby, T., & Zimek, A. (2021). Clustering Refinement. International Journal of Data Science and Analytics, 12(4), 333–353. https://doi.org/10.1007/s41060-021-00275-z
International Journal of Data Science and Analytics
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ISSN:
2364-415X
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Date (published):
Oct-2021
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Number of Pages:
21
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Publisher:
Springer Nature
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Peer reviewed:
Yes
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Keywords:
Computer Science Applications; Applied Mathematics; Modeling and Simulation; Information Systems; Computational Theory and Mathematics; cluster validity; machine learning interpretability; cluster refinemen
en
Abstract:
Advanced validation of cluster analysis is expected to increase confidence and allow reliable implementations. In this work, we describe and test CluReAL, an algorithm for refining clustering irrespective of the method used in the first place. Moreover, we present ideograms that enable summarizing and properly interpreting problem spaces that have been clustered. The presented techniques are built on absolute cluster validity indices. Experiments cover a wide variety of scenarios and six of the most popular clustering techniques. Results show the potential of CluReAL for enhancing clustering and the suitability of ideograms to understand the context of the data through the lens of the cluster analysis. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in unsupervised analysis.
CC BY 4.0
