with macroeconomic indicators as an exogenous variable. By using the k-means clustering based on the monthly rate of return …

-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent … bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization … modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results …

Purpose: This study proposes a new two-stage clustering method to break down the symmetric multiple traveling salesman …-programming model. A comparison with the k-means++ clustering algorithm, one of the most popular clustering algorithms, was made to … study tackles the issue of improving the performance of clustering-based optimization approaches in solving the mTSP by …

may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to … crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset … using only the insured's characteristics and policy information without regard to known mortality. This clustering has the …

We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster … method results in economically more meaningful dynamic clustering patterns. It does so by extending standard cross …-sectional clustering techniques using shrinkage towards previous cluster means. In this way, the different cross-sections in the panel are …

We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data …