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Data are continuously evolving from a huge variety of applications in huge volume and size. They are fast changing, temporally ordered and thus data mining has become a field of major interest. A mining technique such as clustering is implemented in order to process data streams and generate a set of similar objects as an individual group. Outliers generated in this process are the noisy data points that shows abnormal behavior compared to the normal data points. In order to obtain the clusters of pure quality outliers should be efficiently discovered and discarded. In this paper, a concept of pruning is applied on the stream optics algorithm along with the identification of real outliers, which reduces memory consumption and increases the speed for identifying potential clusters.