Data Stream Clustering Algorithms: Challenges and Future Directions
G. Sunitha1, C. Jaswitha2
1G. Sunitha, professor of CSE Department at Sree Vidyanikethan Engineering College, Tirupati, India.
2Jaswitha, PG Scholar from the Department of CSE at Sree Vidyanikethan Engineering College, Tirupati, India.
Manuscript received on 28 August 2019. | Revised Manuscript received on 08 September 2019. | Manuscript published on 30 September 2019. | PP: 3676-3681 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19900981119/2019©BEIESP | DOI: 10.35940/ijitee.K1990.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Data stream, Clustering, BIRCH, K-Means.
Scope of the Article: Streaming Data