Data stream clustering: Difference between revisions

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== Definition ==

Data stream clustering is the task of organizing data points arriving from a continuous and potentially unbounded stream into coherent groups or clusters, under the constraints of limited memory and processing time. Unlike traditional clustering techniques that assume access to the entire dataset, stream clustering must operate incrementally and adaptively as new data arrives.

The problem of data stream clustering is defined as:

The objective of stream clustering is to maintain an up-to-date grouping of data points based on their similarity, while accounting for the constantly evolving nature of the data. Since it is not feasible to store or revisit all incoming data, clustering is often performed over a recent subset of the stream. This is typically achieved using techniques such as sliding windows, which focus on the most recent data points, or decay models, which gradually reduce the importance of older data.

'''Input:''' a sequence of ''n'' points in metric space and an integer ''k''.<br />

'''Output:''' ''k'' centers in the set of the ''n'' points so as to minimize the sum of distances from data points to their closest cluster centers.

Clustering algorithms are designed to summarize data efficiently and update the clustering structure as new points arrive. These algorithms aim to identify dense or coherent regions in the data stream and group similar items together based on proximity or statistical features.

This is the streaming version of the k-median problem.

=== Key Constraints ===

* '''Single-pass Processing''': Due to the high velocity and volume of incoming data, stream clustering algorithms are designed to process each data point only once or a limited number of times.

* '''Limited Memory Usage''': Algorithms operate under strict memory constraints and rely on data summarization techniques, such as micro-clusters or compact data structures, rather than storing all data points.

* '''Real-time Operation''': The system must produce and update clusters in real time or near real time to be applicable in scenarios such as network monitoring or fraud detection.

* '''Concept Drift''': In many applications, the underlying data distribution may change over time. Stream clustering algorithms often incorporate mechanisms to adapt to such non-stationary behavior.

* '''Unlabeled and Unsupervised''': Data stream clustering is generally unsupervised, and labeled data for validation or training is rarely available in real-time environments.

== Algorithms ==