|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 136 - Number 2
|Year of Publication: 2016
|Authors: Pooja Thakkar, Jay Vala, Vishal Prajapati
Pooja Thakkar, Jay Vala, Vishal Prajapati . Survey on Outlier Detection in Data Stream. International Journal of Computer Applications. 136, 2 ( February 2016), 13-16. DOI=10.5120/ijca2016908257
Data mining provides a way for finding hidden and useful knowledge from the large amount of data .usually we find any information by finding normal trends or distribution of data .But sometimes rare event or data object may provide information which is very interesting to us .Outlier detection is one of the task of data mining .It finds abnormal data point or sequence hidden in the dataset .Data stream is unbounded sequence of data with explicit or implicit temporal context .Data stream is uncertain and dynamic in nature. Traditional outlier detection techniques for static data which require whole dataset for modelling is not suitable for data stream because whole data stream cannot be stored. Network intrusion detection ,web click stream analysis ,fraud detection ,fault detection in machines ,sensor data analysis are some of the applications of data stream outlier detection .In this paper, we have described several issues in data stream outlier detection and usual approaches or techniques for finding outlier in data stream .