An Alternative Approach for Binary and Categorical Self-Organizing Maps
Santana, Alessandra [UNIFESP]
Morais, Alessandra [UNIFESP]
Quiles, Marcos G. [UNIFESP]
TypeTrabalho apresentado em evento
Is part of2017 International Joint Conference on Neural Networks (IJCNN)
MetadataShow full item record
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.