The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these …-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose … heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non …

This paper investigates the drivers and dynamics of clustering and inter-project collaboration within the framework of … perception of clustering, the willingness to share information under legal confidentiality, and motivations for engaging with … respondents (48 out of 55) perceive clustering as a valuable asset, indicative of a positive shift in perspectives. Challenges …

(TTI) to study traffic congestion on a macro level (zones) without limiting our research to clustering the zones by …

clustering on the basis of models of Marshall-Arrow-Romer (MAR), Porter, or Jacobs, since proximity is known as a key perquisite … endorse the 'clustering' policy. Consequently, this research aims to examine the spatiality and geography of competition and … follows a clustering development pattern. Through the implementation of a causal research design utilizing both in …