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- W2116772194 abstract "In this paper we present a multi-level approach for extracting well-defined and semantically sound information granules from numerical data. The approach is based on the Double Clustering framework (DC/), which performs two main clustering steps on the data space in order to extract granules qualitatively described in terms of fuzzy sets that meet a number of interpretability constraints. While DC/ can extract information granules with a fixed level of granulation, its multi-level extension, called ML-DC (Multi-Level Double Clustering), can perform granulation of data at different levels, in a hierarchical fashion. At the first level, the whole dataset is granulated. At the second level, data embraced in each first-level granule are further granulated taking into account the context generated by that granule. The hierarchical collection of granules derived via ML-DC is then used to construct a committee of fuzzy inference systems that can approximate any I/O mapping with a good balance between accuracy and interpretability." @default.
- W2116772194 created "2016-06-24" @default.
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- W2116772194 date "2006-01-01" @default.
- W2116772194 modified "2023-09-27" @default.
- W2116772194 title "Balancing Interpretability and Accuracy by Multi-Level Fuzzy Information Granulation" @default.
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- W2116772194 doi "https://doi.org/10.1109/fuzzy.2006.1681999" @default.
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