An Alternative Approach for Binary and Categorical Self-Organizing Maps

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.