In this paper, we have investigated the influence of cluster instability on the performance of layered cluster oriented ensemble classifier. The final contents of clusters in some clustering algorithms like k–means depend on the initialization of clustering parameters like cluster centres. Layered cluster oriented ensemble classifier is based on this philosophy where the base classifiers are trained on clusters generated at multiple layers from random initialization of cluster centres. As the data is clustered into multiple layers some patterns move between clusters (unstable patterns). This instability of patterns brings in diversity among the base classifiers that in turn influences the accuracy of the ensemble classifier. There is thus a connection between the instability of the patterns and the accuracy of layered cluster oriented ensemble classifier. The research presented in this paper aims to find this connection by investigating the influence of unstable patterns on the overall ensemble classifier accuracy as well as diversity among the base classifiers. We have provided results from a number of experiments to quantify this influence.