Data Stream Clustering Algorithms: Challenges and Future Directions
In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area...
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| Published in: | International journal of innovative technology and exploring engineering Vol. 8; no. 11; pp. 3676 - 3681 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
30.09.2019
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| ISSN: | 2278-3075, 2278-3075 |
| Online Access: | Get full text |
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| Summary: | In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area. Recent attention of data stream clustering is through the applications that contain large amounts of streaming data. Data stream clustering is used in many areas such as weather forecasting, financial transactions, website analysis, sensor network monitoring, e-business, telephone records and telecommunications. In case of data stream clustering most popularly used heuristic is K-means and other algorithms like K-medoids and the popular BIRCH are developed. The aim of the abstract is to review the developments and trends of data stream clustering methods and analyze typical DSC algorithms proposed in recent years, such as BIRCH, STREAM, DSTREAM and some more algorithms. |
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| ISSN: | 2278-3075 2278-3075 |
| DOI: | 10.35940/ijitee.K1990.0981119 |