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. |
