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- W2735324717 abstract "One of the most used neural network model for clustering data is the Self-Organizing Map (SOM). Over the years, it has been applied in many areas, from computing to biology, and therefore a wide range of data types have been considered. Originally, the SOM was developed to take real-valued data into account. Thus, learning other data types, such as binary and category data, remains a challenge. This work proposes an alternative and effective modified SOM, to better cluster binary and categorical data." @default.
- W2735324717 created "2017-07-21" @default.
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- W2735324717 date "2017-05-01" @default.
- W2735324717 modified "2023-10-03" @default.
- W2735324717 title "An alternative approach for binary and categorical self-organizing maps" @default.
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- W2735324717 doi "https://doi.org/10.1109/ijcnn.2017.7966174" @default.
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