ArticleCurrent research in food science2023
Recommendations for validating hierarchical clustering in consumer sensory projects.
Article in Current research in food science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Association of physicians' Big Five personality traits with shared decision-making in patients with SLE.Rheumatology (Oxford, England) · 2025Article
- Structural Connectivity Differences Reflect Microstructural Heterogeneity of the Human Insular Cortex.Human brain mapping · 2025Article
- Deciphering consumer perceptions of Kisra prepared from biofortified and non-biofortified sorghum cultivars using the rate-all-that-apply (RATA) method.Frontiers in nutrition · 2025Article
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- Evaluation of antibiotic prescribing for the treatment of male community-acquired urinary tract infections using reimbursement data.PloS one · 2025Article
- Measuring health literacy to inform actions to address health inequities: a cluster analysis approach based on the Australian national health literacy survey.Journal of public health (Oxford, England) · 2024Article
- Identification of Cherry Tomato Volatiles Using Different Electron Ionization Energy Levels.Molecules (Basel, Switzerland) · 2024Article
- Machine learning and analytic hierarchy process integration for selecting a sustainable tractor.Scientific reports · 2024Article
- Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort.Scientific reports · 2024Article
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Abstract
Choosing the proper hierarchical clustering algorithm and number of clusters is always a key question in consumer sensory projects. In many cases, researchers do not publish any reason why it was chosen a given distance measure and linkage rule along with cluster numbers. The reason behind this could be that different cluster validation and comparison techniques give contradictory results in most cases. A complex evaluation to define the proper clustering might be time-consuming and tedious. The paper introduces the clustering of three sensory data sets using different distance metrics and linkage rules for different numbers of clusters. The results of the validation methods deviate, suggesting that clustering depends heavily on the data set in question. Although Euclidean distance, Ward's method seems a safe choice, testing, and validation of different clustering combinations is strongly suggested.
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