structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier …

and a developed branch-and-bound procedure. Moreover, a clustering-based algorithm has been presented that can provide a …

six classes using a hierarchical clustering algorithm based on five selected spatial characteristics. The six categories …

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. …

various non-parametric clustering models to detect outliers, and assesses their effectiveness using different quality criDer … Clustering-Modelle, um auffällige Werte aufzudecken und nach verschiedenen Gütekriterien deren Wirkung zu bewerten. Als geeignet … various non-parametric clustering models to detect outliers, and assesses their effectiveness using different quality criteria …

-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 …

approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in …

environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition … number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices … of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the …