This paper presents a novel three staged clustering algorithm and a new similarity measure. The main objective of the first stage is to create the initial clusters, the second stage is to refine the initial clusters, and the third stage is to refine the initial BASES, if necessary. The novelty of our algorithm originates mainly from three aspects: automatically estimating k value, a new similarity measure and starting the clustering process with a promising BASE. A BASE acts similar to a centroid or a medoid in common clustering method but is determined differently in our method. The new similarity measure is defined particularly to reflect the degree of the relative change between data samples and to accommodate both numerical and categorical variables. Moreover, an additional function has been devised within this algorithm to automatically estimate the most appropriate number of clusters for a given dataset. The proposed algorithm has been tested on 3 benchmark datasets and compared with 7 other commonly used methods including TwoStep, k-means, k-modes, GAClust, Squeezer and some ensemble based methods including k-ANMI. The experimental results indicate that our algorithm identified the appropriate number of clusters for the tested datasets and also showed its overall better clustering performance over the compared clustering algorithms.