Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems

Data mining processes data from different perspectives into useful knowledge, and becomes an important component in designing intelligent decision support systems (IDSS). Clustering is an effective method to discover natural structures of data objects in data mining. Both clustering ensemble and semi-supervised clustering techniques have been emerged to improve the clustering performance of unsupervised clustering algorithms. Cop-Kmeans is a K-means variant that incorporates background knowledge in the form of pairwise constraints. However, there exists a constraint violation in Cop-Kmeans. This paper proposes an improved Cop-Kmeans (ICop-Kmeans) algorithm to solve the constraint violation of Cop-Kmeans. The certainty of objects is computed to obtain a better assignment order of objects by the weighted co-association. The paper proposes a new constrained self-organizing map (SOM) to combine multiple semi-supervised clustering solutions for further enhancing the performance of ICop-Kmeans. The proposed methods effectively improve the clustering results from the validated experiments and the quality of complex decisions in IDSS.