## 4 Discussion  
In this work, a novel unsupervised clustering pipeline has been proposed for lighting research applications. It basically provides a generalized, data-driven framework of sampling diversified light spectra composed of a certain number of primaries to be used for lighting preference studies and correlation analyses. To the authors' best knowledge, this is the first approach ever that, in contrast to the criteria-based manual selection of light spectra known from existing literature, makes use of state-of-the-art feature reduction and clustering algorithms in order to select the most distinct light spectra from a given larger  
Figure 11: Light spectra selected from each sub-cluster of two different main clusters (**a**) and (**b**) by applying a pre-defined median-based filter metric for proof of concept. Figure 12: Parameter representations of the light spectra filtered out of each sub-cluster for a subset of lighting parameters. Labels on the \(x\)-axis represent the corresponding main cluster assignment. set of possible candidates, while still having the option of adhering to certain pre-defined photometric and colorimetric requirements. Summarizing the individual processing steps discussed in detail in the previous sections, Figure 13 eventually gives an overview of the complete algorithmic pipeline of the proposed approach. Starting from unbiased, purely physical features, this orchestrated assembly of algorithms can be used to find representative clusters of most diverse light spectra from an arbitrary set of spectral input data. Optionally, the respective processing pipeline additionally allows for sub-clustering and data filtering based on some pre-selected lighting parameters identified as relevant for the specific research question at hand. Furthermore, the two-dimensional cluster representations provided by applying the UMAP and HDBSCAN algorithms may help to visualize and detect local correlations in the data. Throughout this work, it has been shown that, by applying a convolutional autoencoder to pre-process the spectral input data, the physical information contained within the 400 wavelength bins between 380 nm and 799 nm can efficiently be captured with sufficient accuracy in a 15-dimensional reduced space, where the corresponding MSE loss was \(5.5\times 10^{-5}\). Subsequent feature selection and transformation lead to a further dimensional reduction and an embedded space representation of the relevant spectral information taking into account both the local similarity and the global structure of the data. As an example, it has been show the resulting embedded space can meaningfully be interpreted with respect to the CCT and illuminance distributions of the input light spectra. A density-based clustering approach is able to identify similar light spectra and assign them to corresponding clusters, which by definition are most diverse to each other and, thus, can be used to sample light spectra of maximal diversity. Thus, a sampling of diversified light spectra can be based on selecting candidates from each of these distinct main clusters. Optional sub-clustering can be applied to achieve a further diversification of light spectra within each of the main clusters. A minimal working example of the algorithmic implementation has been prepared and made available for download ([[https://github.com/KliPS/MDPI-Diversified-Light-Spectra](https://github.com/KliPS/MDPI-Diversified-Light-Spectra)]([https://github.com/KliPS/MDPI-Diversified-Light-Spectra](https://github.com/KliPS/MDPI-Diversified-Light-Spectra)), accessed on 24 September 2021). It should be noted that, although a DALI-controlled lighting device was chosen to be used for validation purposes, the proposed methodology constitutes a universal approach in such a way that it is solely based on physical (i.e., spectral) input data, which basically makes it independent of any chosen electrical dimming strategy. Moreover, it is not limited by the complexity of the spectral input data.