Clustering algorithms have been widely used artificial intelligence, data mining and machine learning, etc. It is unsupervised classification and is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, to analysis interval data needs Type II fuzzy logical. Therefore, the interval fuzzy c-means clustering method was proposed to deal with symbolic interval data. However, it still has noisy and outliers problems on these symbolic interval data. Hence, we propose Hausdorff distance measure based interval fuzzy possibilistic c-means clustering algorithm to overcome the interval fuzzy c-means clustering algorithm for the symbolic interval data clustering in noisy and outlier environments. From the simulation results, the proposed Hausdorff distance measure based interval fuzzy possibilistic c-means clustering algorithm has better performance than interval fuzzy c-means clustering algorithm.