Clustering-Based Reference Set Reduction for k-Nearest Neighbor

摘要

Response Modeling is concerned with computing the likelihood of a customer to respond to a marketing campaign. A major problem encountered in response modeling is huge volume of data or patterns. The k-NN has been used in various classification problems for its simplicity and ease of implementation. However, it has not been applied to problems for which fast classification is needed since the classification time rapidly increases as the size of reference set increases. In this paper, we propose a clustering-based preprocessing step in order to reduce the size of reference set. The experimental results showed an 85% decrease in classification time without a loss of accuracy.