A1 Journal article (refereed)
Toolbox for Distance Estimation and Cluster Validation on Data With Missing Values (2022)
Niemelä, M., Äyrämö, S., & Kärkkäinen, T. (2022). Toolbox for Distance Estimation and Cluster Validation on Data With Missing Values. IEEE Access, 10, 352-367. https://doi.org/10.1109/ACCESS.2021.3136435
JYU authors or editors
Publication details
All authors or editors: Niemelä, Marko; Äyrämö, Sami; Kärkkäinen, Tommi
Journal or series: IEEE Access
eISSN: 2169-3536
Publication year: 2022
Volume: 10
Pages range: 352-367
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/ACCESS.2021.3136435
Research data link: https://github.com/markoniem/nanclustering_toolbox
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/79601
Abstract
Missing data are unavoidable in the real-world application of unsupervised machine learning, and their nonoptimal processing may decrease the quality of data-driven models. Imputation is a common remedy for missing values, but directly estimating expected distances have also emerged. Because treatment of missing values is rarely considered in clustering related tasks and distance metrics have a central role both in clustering and cluster validation, we developed a new toolbox that provides a wide range of algorithms for data preprocessing, distance estimation, clustering, and cluster validation in the presence of missing values. All these are core elements in any comprehensive cluster analysis methodology. We describe the methodological background of the implemented algorithms and present multiple illustrations of their use. The experiments include validating distance estimation methods against selected reference methods and demonstrating the performance of internal cluster validation indices. The experimental results demonstrate the general usability of the toolbox for the straightforward realization of alternate data processing pipelines. Source code, data sets, results, and example macros are available on GitHub. https://github.com/markoniem/nanclustering_toolbox
Keywords: machine learning; algorithms; data; quality; validation; clusters; data processing; modelling (representation)
Free keywords: missing values; distance estimation; clustering; cluster validation
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 2
- Learning and Cognitive Sciences (Faculty of Information Technology IT) LEACS
- Human and Machine based Intelligence in Learning (Faculty of Information Technology IT) HUMBLE
- Computing, Information Technology and Mathematics (Faculty of Information Technology IT) CITM
- Computational Science (Faculty of Information Technology IT) LASK
- Engineering (Faculty of Information Technology IT) OHTE; Formerly Software and Communications Engineering