Outlier detection over data streams is an important task in data mining. It has various applications such as fraud detection, public health, and computer network security. Many approaches have been proposed for outlier detection over data streams such as distance-,clustering-, density-, and learning-based approaches. In this paper, we are interested in the densitybased outlier detection over data streams. Specifically, we propose an improvement of DILOF, a recent density-based algorithm. We observed that the main disadvantage of DILOF is that its summarization method has many drawbacks such as it takes a lot of time and the algorithm accuracy is significant degradation. Our new algorithm is called DILOF^C that utilizing an efficient summarization method. Our performance study shows that DILOF^C outperforms DILOF in terms of total response time and outlier detection accuracy.