تقرير
Co-clustering based exploratory analysis of mixed-type data tables
| العنوان: | Co-clustering based exploratory analysis of mixed-type data tables |
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| المؤلفون: | Bouchareb, Aichetou, Boullé, Marc, Clérot, Fabrice, Rossi, Fabrice |
| المصدر: | Advances in Knowledge Discovery and Management, 834, Springer International Publishing, pp.23-41, 2019, Studies in Computational Intelligence |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Machine Learning |
| الوصف: | Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-18129-1_2 |
| URL الوصول: | http://arxiv.org/abs/2212.11728 |
| رقم الأكسشن: | edsarx.2212.11728 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-18129-1_2 |
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