Paper Title
Evaluation of Hotels’ Customers based on Data Mining and MCDM Approaches

This study aims to present a new recency, frequency, monetary (RFM) model named Recency, Monetary and Duration (RMD) to classify hotel customers and to identify valuable customers by a hybrid approach consisting of data mining and multi-layer decision-making methods. The data is gathered from a hotel in an emerging economy of Iran. Three cluster validation indices are applied to determine the best number of clusters, and the K-means algorithm is employed to cluster customers. RMD attributes are extracted for each customer. Placed on this model, customers are grouped into divergent clusters, and then hidden information is extracted by association rules. Furthermore, clusters are prioritised by the technique for order of preference by similarity to the ideal solution (TOPSIS) and customer lifetime value (CLV). Eventually, unique marketing strategies are recommended for each cluster. Keywords – RMD, K-means, Association rules, TOPSIS, CLV