A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations. Souiden, I., Brahmi, Z., & Toumi, H. In Madureira, A. M., Abraham, A., Gamboa, D., & Novais, P., editors, Intelligent Systems Design and Applications, pages 372–383, Cham, 2017. Springer International Publishing. doi abstract bibtex 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.
@inproceedings{souiden_survey_2017,
address = {Cham},
title = {A {Survey} on {Outlier} {Detection} in the {Context} of {Stream} {Mining}: {Review} of {Existing} {Approaches} and {Recommadations}},
isbn = {978-3-319-53480-0},
shorttitle = {A {Survey} on {Outlier} {Detection} in the {Context} of {Stream} {Mining}},
doi = {10.1007/978-3-319-53480-0_37},
abstract = {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.},
language = {en},
booktitle = {Intelligent {Systems} {Design} and {Applications}},
publisher = {Springer International Publishing},
author = {Souiden, Imen and Brahmi, Zaki and Toumi, Hajer},
editor = {Madureira, Ana Maria and Abraham, Ajith and Gamboa, Dorabela and Novais, Paulo},
year = {2017},
pages = {372--383},
}
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