A new knowledge based constrained clustering approach: Theory and application in direct marketing
Graphical abstractDisplay Omitted HighlightsThis paper proposes a straightforward way to apply constrained clustering.Soft attribute level constraints are generated based on feature order preferences.Practitioners can formalize and use their a priori knowledge.A methodology implementing this approach is applied in a direct marketing context. Clustering has always been an exploratory but critical step in the knowledge discovery process. Often unsupervised, the clustering task received a huge interest when reinforced by different kinds of inputs provided by the user. This paper presents an approach giving the possibility to incorporate business knowledge in order to guide the clustering algorithm. A formalization of the fact that an intuitive a priori prioritization of the variables might exist, is presented in this paper and applied in a direct marketing context using recent data. By providing the analyst with a new approach offering different clustering perspectives, this paper proposes a straightforward way to apply constrained clustering with soft attribute level constraints based on feature order preferences.
Appl. Soft Comput.