In order to develop an efficient framework for global screening in the material exploration, we performed a clustering analysis of machine learning on the multi-dimensional thermophysical properties of the liquid substances. Data mining using a self-organizing map (SOM) based on the unsupervised learning was employed to project high-dimensional thermophysical data onto a low-dimensional space. Here we adopted 98 liquid substances with eight thermo-physical properties for the SOM training in order to group the liquid substances. The present SOM-clustering approach properly categorized liquid substances according to the chemical species characterized by the functional groups.