Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets

Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets

On the chemical state of Co oxide electrocatalysts during alkaline water splitting.

Resonant inelastic X-ray scattering and high-resolution X-ray absorption spectroscopy were used to identify the chemical state of a Co electrocatalyst in situ during the oxygen evolution reaction. After anodic electrodeposition onto Au(111) from a Co(2+)-containing electrolyte, the chemical environment of Co can be identified to be almost identical to CoOOH. With increasing potentials, a subtle increase of the Co oxidation state is observed, indicating a non-stoichiometric composition of the working OER catalyst containing a small fraction of Co(4+) sites. In order to confirm this interpretation, we used density functional theory with a Hubbard-U correction approach to compute X-ray absorption spectra of model compounds, which agree well with the experimental spectra. In situ monitoring of catalyst local structure and bonding is essential in the development of structure-activity relationships that can guide the discovery of efficient and earth abundant water splitting catalysts

On the chemical state of Co oxide electrocatalysts during alkaline water splitting.

Resonant inelastic X-ray scattering and high-resolution X-ray absorption spectroscopy were used to identify the chemical state of a Co electrocatalyst in situ during the oxygen evolution reaction. After anodic electrodeposition onto Au(111) from a Co(2+)-containing electrolyte, the chemical environment of Co can be identified to be almost identical to CoOOH. With increasing potentials, a subtle increase of the Co oxidation state is observed, indicating a non-stoichiometric composition of the working OER catalyst containing a small fraction of Co(4+) sites. In order to confirm this interpretation, we used density functional theory with a Hubbard-U correction approach to compute X-ray absorption spectra of model compounds, which agree well with the experimental spectra. In situ monitoring of catalyst local structure and bonding is essential in the development of structure-activity relationships that can guide the discovery of efficient and earth abundant water splitting catalysts

Electrochemical Oxidation of Size-Selected Pt Nanoparticles Studied Using in Situ High-Energy-Resolution X‑ray Absorption Spectroscopy

High-energy-resolution fluorescence-detected X-ray absorption spectroscopy (HERFD-XAS) has been applied to study the chemical state of ∼1.2 nm size-selected Pt nanoparticles (NPs) in an electrochemical environment under potential control. Spectral features due to chemisorbed hydrogen, chemisorbed O/OH, and platinum oxides can be distinguished with increasing potential. Pt electro-oxidation follows two competitive pathways involving both oxide formation and Pt dissolution