Features Clustering Around Latent Variables for High Dimensional Data
نویسندگان
چکیده
Clustering of variables is the task grouping similar into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies. In present study, we combine two methods features clustering around latent (CLV) algorithm k-means based co-clustering (kCC). Indeed, classical CLV cannot applied to high dimensional data because this approach becomes tedious when number increases.
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ژورنال
عنوان ژورنال: E3S web of conferences
سال: 2021
ISSN: ['2555-0403', '2267-1242']
DOI: https://doi.org/10.1051/e3sconf/202129701070