Kseniia Kurishchenko

Kseniia Kurishchenko
Copenhagen Business School · Department of Economics

About

3
Publications
1,024
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6
Citations
Citations since 2016
3 Research Items
6 Citations
20162017201820192020202120220.00.51.01.52.02.53.0
20162017201820192020202120220.00.51.01.52.02.53.0
20162017201820192020202120220.00.51.01.52.02.53.0
20162017201820192020202120220.00.51.01.52.02.53.0

Publications

Publications (3)
Preprint
Full-text available
In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the corresponding cluster, true posi...
Article
Full-text available
In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the corresponding cluster, true posi...
Preprint
Full-text available
In this paper, we make Cluster Analysis more interpretable with a new approach that simultaneously allocates individuals to clusters and gives rule-based explanations to each cluster. The traditional homogeneity metric in clustering, namely the sum of the dissimilarities between individuals in the same cluster, is enriched by considering also, for...

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Projects

Project (1)
Project
NeEDS (Network of European Data Scientists) provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.