A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations

• Published in: Intelligent Systems Design and Applications Vol. 557; pp. 372 - 383
• Main Authors: Souiden, Imen, Brahmi, Zaki, Toumi, Hajer
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Advances in Intelligent Systems and Computing
• Subjects: Artificial intelligence; Data stream; Data stream mining; Outlier detection
• ISBN: 9783319534794; 3319534793
• ISSN: 2194-5357; 2194-5365
• DOI: 10.1007/978-3-319-53480-0_37

Generally, extracting only expected knowledge from data is not sufficient since unexpected ones can hide useful information concerning the data behavior. These information can be further used to optimize the current state. This has lead to the outlier detection. It refers to the data mining task that aims to find abnormal points or sequence of data hidden in the dataset. In fact, due to the emergence of new technologies, applications often generate and consume data in form of streams. This data differs from the static one. Therefore, traditional techniques cannot be used. Hence, convenient ones suitable to the data stream nature must be applied. In this paper, we will review different techniques of outlier detection in the data streams. In addition, we shall describe different approaches based on these techniques in order to establish a comparative study based on different criterion. This study aims to help users and facilitates the choice of the appropriate algorithm for a certain context.