In computer science, data stream 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 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.
History
The problem of data stream clustering has recently attracted much attention for its applicability to emerging applications that involve a large amount of streaming data such as network flows, sensor data, and web click streams. One of the first results on data streams was due to Munro and Paterson [1] but the model was formalized much later by Henzinger, Raghavan, and Rajagopalan [2]. The method used for data stream clustering is the k-means
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
References
<references>
Notes
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5222900&isnumber=5222860&tag=1
http://www.springerlink.com/content/uc06wwfpc8wl04wf/fulltext.pdf