Data stream clustering: Difference between revisions

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In [[computer science]], data stream [[clustering analysis | clustering]] is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied under the [[streaming algorithm | data stream model]] of computation and the objective is, given a sequence of points, to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time.

<!-- in contrary to the traditional clustering where data are static. -->

== History ==

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== Algorithms ==

Many algorithms have been proposed for the data stream clustering problem. The performance of an algorithm that operates on data streams is measured by three basic factors:

#* The number of passes the algorithm must make over the stream.

#* The available memory.

#* The running time of the algorithm.

These algorithms have many similarities with [[online algorithms]] but they are not identical. Unlike online algorithms, algorithms for data stream clustering have only a bounded amount of memory available and they may be able to take action after a group of points arrives while online algorithms are required to take action after each point arrives.

Some of the most well-known algorithms used for data stream clustering include:

* BIRCH

* COBWEB

* STREAM

===BIRCH===

===COBWEB===

===STREAM===

== References ==