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The aim of this paper is to explore the advantages and the challenges of a \u2018domain-led approach\u2019 versus a data-driven approach to a k-means clustering experiment. For the purpose of this experiment, clinical experts in heart failure selected variables to be used during the k-means clustering, whilst during the \u2018data-driven approach\u2019 feature selection was performed by applying principal component analysis to the multidimensional dataset. Six out of seven features selected by physicians were amongst 26 features that contributed most to the significant principal components within the k-means algorithm. The data-driven approach showed advantage over the domain-led approach for feature selection by removing the risk of bias that can be introduced by domain experts. Whilst the \u2018domain-led approach\u2019 may potentially prohibit knowledge discovery that can be hidden behind variables not routinely taken into consideration as clinically important features, the domain knowledge played an important role at the interpretation stage of the clustering experiment providing insight into the context and preventing far fetched conclusions. The \u201cdata-driven approach\u201d was accurate in identifying clusters with distinct features at the physiological level. 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