Data stream clustering: Difference between revisions

Content deleted Content added
Aasimayaz (talk | contribs)
m Updated the Definition
Tags: Reverted Visual edit
Aasimayaz (talk | contribs)
m Modified the definition of the topic
Tags: Reverted Visual edit
Line 1:
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.
In [[computer science]], '''data stream clustering''' is defined as the [[cluster analysis|clustering]] of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a [[streaming algorithm]] and the objective is, given a sequence of points, to construct a good clustering of the stream, using a small amount of memory and time. <!-- in contrary to the traditional clustering where data are static. -->
 
== History ==