Towards a new framework for clustering in a mixed data space: Case of gasoline service stations segmentation in Morocco

Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, data sets with mixed types of attributes are common in real life data mining applications. In this paper, we introduce a new framework for clustering mixed data which is based on Random Forest dissimilarity and PAM clustering. Then we apply this framework to segment market of services stations in Morocco to identify features that most influence on profit of each service station.