Outlier detection in stream data by clustering method

Abstract

The fundamental and active research problem in a lot of fields is outlier detection. It is involved many applications. A lot of these methods based on distance measure. But for stream data these methods are not efficient. Most of the previous work on outlier detection declares online outlier and these have less accuracy and it may be lead to a wrong decision. moreover the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. The aim of this study is to present an algorithm to detect outlier in stream data by clustering method that concentrate to find real outlier in period of time. It is considered some outlier that has received in previous time and find out real outlier in stream data. The accuracy of this method is more than other methods.