A Batch Learning Vector Quantization Algorithm for Categorical Data

Learning vector quantization (LVQ) is a supervised learning algorithm for data classification. Since LVQ is based on prototype vectors, it is a neural network approach particularly applicable in non-linear separation problems. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for mixed numerical and categorical data. Experiments on various data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to deal with categorical data.

Authors

• Ning Chen
• Nuno C. Marques

Keywords

• Learning vector quantization
• Self-organizing map
• Categorical
• Batch SOM