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Mining streaming data is an essential task in many applications such as network intrusion, marketing, manufacturing and others. The main challenge in the streaming data model is its unbounded size. This makes it difficult to run traditional mining techniques on this model. In this paper, we propose a new approach for mining emerging patterns (EPs) in data streams. Our method is based on mining EPs in a selective manner. EPs are those itemsets whose frequencies in one class are significantly higher than their frequencies in the other classes. Our experimental evaluation proves that our approach is capable of gaining important knowledge from data streams.