classification model is established based on a combination of clustering, feature selection, decision trees, and genetic algorithm … techniques. We used clustering and feature selection techniques to pre-process the input samples to construct the decision trees …

hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal … with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models …: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual …

The structure of alliance networks influences their potential for knowledge creation. Dense local clustering provides … firms embedded in alliance networks that exhibit both high clustering and high reach (short average path lengths to a wide …

associated with the fee-paying structure of IPOs into account and discount the offer price accordingly. By timely clustering of …

attitude to risk responses, there are 18 clusters, with similar but not identical membership, depending on which clustering …

parity strategies across economic clusters. For handling overly complex correlation matrices, hierarchical clustering … ingredients to aid portfolio diversification. We explain the intuition and nature of hierarchical clustering techniques in the …

We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model …