This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.