Creating Product Maps with Self-Organizing Maps for Purchase Decision Making

We propose a way of creating product maps with self-organizing maps (SOMs) for purchase decision making. We previously proposed a way of purchase decision support using SOMs and the Analytic Hierarchy Process (AHP). We provided several class boundaries, which divided the input features into several classes before creating self-organizing product maps. Because the number of classes and their boundaries depended on the person classifying the classes, the product maps were not always the same. In this paper, we first provide two class boundaries, which divide the range between the maximum and minimum of an input feature value into three equal parts. Second, we create self-organizing product maps using the classified data inputs. We applied our way to five kinds of products and confirmed its effectiveness.

• Type: Journal
• Year: 2010
• Where: MLDM
• Authors: Kazuhiro Kohara, Tetsuya Tsuda
• Keywords: Class Boundaries, Machine Learning, MLDM 2010, Product Maps, Purchase Decision