Anomaly Detection in Large-Scale Data using Clustering and Outlier Analysis
DOI:
https://doi.org/10.15662/cm92ap13Keywords:
Anomaly Detection, Clustering Algorithms, Outlier Analysis, Unsupervised Learning, Isolation Forest, Local Outlier Factor, Large-Scale Data, High-Dimensional Data, Density-Based Methods, Data MiningAbstract
With the exponential growth of large-scale data across domains such as finance, healthcare, cybersecurity, and social networks, anomaly detection has emerged as a critical tool for identifying rare, abnormal patterns that may signify fraud, intrusions, or system failures. In 2020, the combination of clustering techniques and outlier analysis offered effective frameworks for detecting anomalies in massive datasets where labeled data is often scarce. This paper explores state-of-the-art clustering-based methods such as k-means, DBSCAN, and Hierarchical Clustering, integrated with statistical and density-based outlier detection to identify anomalous instances.
Unsupervised and semi-supervised learning dominated the anomaly detection space in 2020 due to the lack of labeled
anomalies. Algorithms such as Isolation Forest, Local Outlier Factor (LOF), and hybrid approaches leveraging ensemble clustering were widely adopted. These techniques showed significant effectiveness in distributed and high-dimensional data environments, particularly when optimized for real-time or near-real-time applications. The literature demonstrates that clustering enhances anomaly detection by grouping similar behavior patterns, while outlier detection quantifies the deviation of specific data points from these patterns.
This paper contributes a comparative study of clustering-outlier approaches, analyzing their scalability, sensitivity to noise, interpretability, and adaptability across domains. Results show that combining clustering with density-based outlier analysis enhances detection accuracy while reducing false positives. Applications in 2020 included network intrusion detection, fraud analytics, IoT systems monitoring, and medical diagnostics.
Despite progress, challenges remain in selecting optimal clustering parameters, handling dynamic data streams, and integrating domain knowledge. This work highlights the strengths, limitations, and future prospects of clustering-based anomaly detection systems in large-scale, high-dimensional environments.
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