A comparative study of hard clustering algorithms for vegetation data. (8th June 2021)

• Record Type: Journal Article
• Authors: Pakgohar, Naghmeh; Eshaghi Rad, Javad; Gholami, Gholamhossein; Alijanpour, Ahmad; Roberts, David W.
• Editors: Botta‐Dukát, Zoltán
• Is Part Of: Journal of vegetation science. Volume 32:Number 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-08
• Subjects: clustering -- distance measure -- GLMM -- hierarchical -- non‐hierarchical -- OPTPART -- OPTSIL -- simulated data set; Plant ecology -- Periodicals; Plant communities -- Periodicals; Plant populations -- Periodicals; 581.7
• DOI: 10.1111/jvs.13042
• Languages: English
• ISSNs: 1100-9233

Abstract

Abstract: Questions: Which clustering algorithms are most effective according to different cluster validity evaluators? Which distance or dissimilarity measure is most suitable for clustering algorithms? Location: Hyrcanian forest, Iran (Asia), Virginia region forest, United States (North America), beech forests, Ukraine (Europe). Methods: We tested 25 clustering algorithms with nine vegetation data sets comprised of three real data sets and six simulated data sets exhibiting different cluster separation values. The clustering algorithms included both hierarchical and non‐hierarchical partitioning. Five evaluators were employed on each cluster solution to evaluate different clustering algorithms. Algorithms were ranked from best to worst on each clustering evaluator for each data set. Results: The comparison revealed that the OPTSIL initiated from a Flexible‐ β (−0.25) solution achieved particularly good performance. We also found that Ward's method and Flexible‐ β (−0.1) implementations were accurate. K ‐means with Hellinger distance was superior to Partitioning Around Medoids (PAM) algorithms. Accordance between distance measures and clustering algorithms was also observed. Bray–Curtis dissimilarity combined with a range of clustering algorithms was successful in most cases. Bray–Curtis dissimilarity proved superior to other distance measures for heterogeneous data sets. Conclusions: All in all, the results demonstrate that choosing the most suitable method before clustering is critical for achieving maximally interpretable clusters. The complexity of vegetation data sets makes this issue even more important. The choice of distance measure had more effect than the choice of clustering method on the quality of results. Our results illustrate that OPTSIL Flexible‐ β (−0.1) and OPTPART could prove superior to alternative conventional clustering algorithms when internal evaluation criteria are used to optimize clustering. Abstract : We tested 25 clustering algorithms with three real data sets and six simulated data sets. The clustering algorithms included both hierarchical and non‐hierarchical partitioning. Our results illustrated that OPTSIL Flexible‐β (−0.1) and OPTPART could prove superior to alternative conventional clustering algorithms when internal evaluation criteria were used to optimize clustering. Bray–Curtis dissimilarity proved superior to other distance measures for heterogeneous data sets.