Anomaly detection

# Anomaly detection > OpenAlex Metadata Hub · https://openalex.org/W2122646361 ## Bibliographic - **DOI:** 10.1145/1541880.1541882 - **Year:** 2009 - **Citations:** 11176 - **Open Access:** No (closed) - **License:** — - **Source:** https://doi.org/10.1145/1541880.1541882 ## Authors - Varun Chandola - Arindam Banerjee - Vipin Kumar ## Abstract Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. ## Keywords Computer science, Anomaly detection, Domain (mathematical analysis), Key (lock), Anomaly (physics), Data mining, Data science, Artificial intelligence, Machine learning, Mathematics ## Concepts - Computer science - Anomaly detection - Domain (mathematical analysis) - Key (lock) - Anomaly (physics) - Data mining - Data science - Artificial intelligence - Machine learning - Mathematics - Condensed matter physics - Mathematical analysis - Physics - Computer security --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Anomaly detection” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techni… Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.