Genetic-based Summarization for Local Outlier Detection in Data Stream
| Autor: | Arabi Keshk, Mohamed Sakr, Walid Atwa |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data stream
Control and Optimization Computer Networks and Communications Computer science computer.software_genre Automatic summarization Computer Science Applications Human-Computer Interaction Artificial Intelligence Modeling and Simulation Signal Processing Anomaly detection Data mining computer |
| Zdroj: | International Journal of Intelligent Systems and Applications. 13:58-68 |
| ISSN: | 2074-9058 2074-904X |
| DOI: | 10.5815/ijisa.2021.01.05 |
| Popis: | Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data. |
| Databáze: | OpenAIRE |
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