Using expert's rules as background knowledge in the ClusDM methodology

In complex domains it is usually quite difficult to introduce context information. However, sometimes that information should be taken into account to make decisions, because it provides some relevant knowledge that cannot be expressed using an attribute-value representation. This is the case of the determination of risk of contamination of soils. In this paper, we propose to use conjunctive rules to introduce additional background knowledge to a MCDM sorting method called ClusDM. ClusDM is based on the aggregation of the data with unsupervised clustering techniques. The paper presents a new algorithm to incorporate rules to guide the clustering process in a semi-supervised way. The paper also describes how it works in the case sorting a set of possible contaminated soils, and compares the results obtained by ClusDM when rules are used or not.