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Outlier detection targets those exceptional data whose pattern is rare and lie in low density regions. In this paper, under the assumption of complete spatial randomness inside clusters, we propose an MDV (Multi-scale Deviation of the Volume) approach to identifying outliers. In addition to...
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clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational ….e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of …
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in terms of higher education systems. The clustering shows, that - with some exceptions (notably the United Kingdom and …
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: Based on an online survey of 40 participants, a clustering analysis was conducted using the unsupervised learning method and … hierarchical clustering (R and R Studio). Results: Five clusters representing different profiles were derived, showing how actors …
Persistent link: https://www.econbiz.de/10015194805
In the present paper we use a balanced bank panel data set to obtain an inference on two dimensions of the asymmetric response of bank lending to interest rate changes. The cross-sectional dimension is captured by group-specific parameters whereby each bank's group membership is estimated along...
Persistent link: https://www.econbiz.de/10013370002
's correlation coefficient, time trend analysis and clustering procedures. Data from Organization for Economic Co-operation and … (CSE). We compared the results of two agglomerative clustering methods and identified groups of similar countries on the …
Persistent link: https://www.econbiz.de/10013400223
mesoregions. The study used a four-stage methodology, compared the results of two agglomerative clustering methods, and identified …
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-means objective to regression settings. As the regularization parameter m approaches 1, the fuzzy clustering objective converges to …
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