in condition monitoring. The identification of operation profiles during production is therefore important. Clustering …-and-effect relationship between the operation regimes and the wear level of components. In this study unsupervised clustering technique was …

, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering …

multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic … to quantify feature importance for clustering methods, finding that size drives differences across classical industries … while book-to-market and financial liquidity variables matter for clustering-based sectors. …

-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical … prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second …