TY - JOUR
T1 - An analysis framework for clustering algorithm selection with applications to spectroscopy
AU - Crase, Simon
AU - Thennadil, Suresh N.
N1 - Funding Information:
YES SC is supported through a
scholarship by the Commonwealth of Australia as
represented by the Defence Science and
Technology Group of the Department of Defence and by an Australian Government Research
Training Program (RTP) Scholarship. www.dst.
defence.gov.au www.dese.gov.au/research-blockgrants/research-training-program Defence Science
and Technology group provided the explosives
datasets for this study. The funders had no role in
study design, decision to publish, or preparation of
the manuscript.
PY - 2022/3
Y1 - 2022/3
N2 - Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is being applied to. There is no universally best clustering algorithm, and hence, there are numerous clustering algorithms available with different performance characteristics. This raises the problem of how to select an appropriate clustering algorithm for the given analytical purposes. We present and validate an analysis framework to address this problem. Unlike most current literature which focuses on characterizing the clustering algorithm itself, we present a wider holistic approach, with a focus on the user's needs, the data's characteristics and the characteristics of the clusters it may contain. In our analysis framework, we utilize a softer qualitative approach to identify appropriate characteristics for consideration when matching clustering algorithms to the intended application. These are used to generate a small subset of suitable clustering algorithms whose performance are then evaluated utilizing quantitative cluster validity indices. To validate our analysis framework for selecting clustering algorithms, we applied it to four different types of datasets: three datasets of homemade explosives spectroscopy, eight datasets of publicly available spectroscopy data covering food and biomedical applications, a gene expression cancer dataset, and three classic machine learning datasets. Each data type has discernible differences in the composition of the data and the context within which they are used. Our analysis framework, when applied to each of these challenges, recommended differing subsets of clustering algorithms for final quantitative performance evaluation. For each application, the recommended clustering algorithms were confirmed to contain the top performing algorithms through quantitative performance indices.
AB - Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is being applied to. There is no universally best clustering algorithm, and hence, there are numerous clustering algorithms available with different performance characteristics. This raises the problem of how to select an appropriate clustering algorithm for the given analytical purposes. We present and validate an analysis framework to address this problem. Unlike most current literature which focuses on characterizing the clustering algorithm itself, we present a wider holistic approach, with a focus on the user's needs, the data's characteristics and the characteristics of the clusters it may contain. In our analysis framework, we utilize a softer qualitative approach to identify appropriate characteristics for consideration when matching clustering algorithms to the intended application. These are used to generate a small subset of suitable clustering algorithms whose performance are then evaluated utilizing quantitative cluster validity indices. To validate our analysis framework for selecting clustering algorithms, we applied it to four different types of datasets: three datasets of homemade explosives spectroscopy, eight datasets of publicly available spectroscopy data covering food and biomedical applications, a gene expression cancer dataset, and three classic machine learning datasets. Each data type has discernible differences in the composition of the data and the context within which they are used. Our analysis framework, when applied to each of these challenges, recommended differing subsets of clustering algorithms for final quantitative performance evaluation. For each application, the recommended clustering algorithms were confirmed to contain the top performing algorithms through quantitative performance indices.
UR - http://www.scopus.com/inward/record.url?scp=85127398745&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0266369
DO - 10.1371/journal.pone.0266369
M3 - Article
C2 - 35358292
AN - SCOPUS:85127398745
SN - 1932-6203
VL - 17
SP - 1
EP - 24
JO - PLoS One
JF - PLoS One
IS - 3 March
M1 - e0266369
ER -