Clustering Techniques for Streaming Dynamic Nature of Data

Authors

  • Vanguru S Assistant Professor, Sri Venkateswara Engineering College, Suryapet
  • Merugu A Dept. of Computer science and Engineering, Sri Venkateswara Engineering College, Suryapet
  • Reddy YG Dept. of Computer science and Engineering, Sri Venkateswara Engineering College, Suryapet

Keywords:

Streaming data, Data Stream mining, Dynamic data, Clustering

Abstract

Nowadays many applications are generating streaming data for an example real-time surveillance, internet traffic, sensor data, health monitoring systems, communication networks, online transactions in the financial market and so on. Data Streams are temporally ordered, fast changing, massive, and potentially infinite sequence of data. Data Stream mining is a very challenging problem. This is due to the fact that data streams are of tremendous volume and flows at very high speed which makes it impossible to store and scan streaming data multiple time. Concept evolution in streaming data further magnifies the challenge of working with streaming data.

Clustering is a data stream mining task which is very useful to gain insight of data and data characteristics. Clustering is also used as a pre-processing step in over all mining process for an example clustering is used for outlier detection and for building classification model. In this paper we will focus on the challenges and necessary features of clustering techniques for streaming dynamic nature of data. Streaming data behaviour keeps on changing over time. Clustering model developed on partial data stream must be updated with new incoming data.

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Published

2024-02-26

How to Cite

Vanguru, S., Merugu, A., & Reddy, Y. (2024). Clustering Techniques for Streaming Dynamic Nature of Data. COMPUSOFT: An International Journal of Advanced Computer Technology, 4(12), 2027–2029. Retrieved from https://ijact.in/index.php/j/article/view/355

Issue

Section

Original Research Article

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