Description: Overlapping clusters are a phenomenon in data analysis where two or more groups of data share some common points, complicating the identification of anomalies. This overlap can make it challenging to distinguish between normal and anomalous data, as data points belonging to different clusters may exhibit similar characteristics. In the context of anomaly detection, overlapping clusters present a significant challenge, as traditional clustering techniques may not be effective. For instance, if a data point lies on the boundary between two clusters, it can be difficult to determine whether it is an outlier or simply a data point that belongs to both groups. This ambiguity can lead to errors in classification and, consequently, incorrect decisions based on data analysis. Understanding overlapping clusters is crucial for improving the accuracy of anomaly detection models, as it enables analysts and data scientists to develop more sophisticated strategies to handle the inherent complexity in various datasets, where interactions between different groups may be more common than previously thought.
