期刊
IEEE ACCESS
卷 10, 期 -, 页码 352-367出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3136435
关键词
Covariance matrices; Estimation; Clustering algorithms; Task analysis; Data preprocessing; Convergence; Clustering methods; Missing values; distance estimation; clustering; cluster validation
资金
- Academy of Finland [311877, 315550]
- Academy of Finland (AKA) [315550, 315550] Funding Source: Academy of Finland (AKA)
The study introduces a new toolbox for handling data with missing values, which includes functions for data preprocessing, distance estimation, clustering, and cluster validation, providing core elements for comprehensive cluster analysis methodologies.
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
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