Description: Outlier clustering is an analytical process that focuses on identifying and grouping data points that significantly deviate from the norm in a dataset. These outliers can result from measurement errors, natural variations, or rare events. The goal of clustering is not only to detect these unusual values but also to understand their characteristics and similarities. This approach allows analysts and data scientists to uncover hidden patterns and relationships in the data that may not be immediately apparent. Through clustering techniques such as K-means or DBSCAN, outliers can be classified into groups that share common features, thereby facilitating their analysis. The relevance of this process lies in its ability to improve data quality, optimize predictive models, and provide valuable insights across various applications, from fraud detection to industrial system monitoring. In summary, outlier clustering is an essential tool in anomaly detection, enabling organizations to make informed decisions based on more accurate and representative data.
