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In computer science, '''data stream [[Cluster analysis|clustering]]''' refers to the process of grouping data points that arrive in a continuous, rapid, and potentially unbounded sequence—such as telephone call logs, multimedia streams, or financial transactions—into meaningful clusters. It is a form of real-time, [[unsupervised learning]] specifically designed to handle the unique challenges posed by streaming environments, including limited memory, single-pass constraints, and evolving data distributions (concept drift). Unlike traditional clustering algorithms that operate on static, finite datasets, data stream clustering must make immediate decisions with partial information and cannot revisit previous data points. This makes it essential in time-sensitive domains such as network intrusion detection, real-time [[Recommender system|recommendation systems]], and sensor-based monitoring. Typically framed within the streaming algorithms paradigm, the goal of data stream clustering is to produce accurate and adaptable clusterings using limited computational resources, while maintaining responsiveness to shifts in the data over time.
== History ==
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