Graph Analysis with Big-data

Authors

  • Kalpana Department of Computer Science, PeriyarUniversity , India

Keywords:

MIMO, NLP, RGA, PIP

Abstract

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model from the data mining perspective. The planning of several optimal tuning processes, the comparison of different designs (through graphics or the numeric results obtained), and the management of data files saved during the planned optimal tunings process. The developed tool was made available to students for them to solve a practical problem and, subsequently, the impact of its use was evaluated. There are techniques to learn the categories (clustering). Methods of pattern recognition are useful in many applications such as information retrieval, data mining.

References

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Published

2025-11-11

How to Cite

[1]
Kalpana, “Graph Analysis with Big-data”, Int. J. Comp. Sci. Eng., vol. 3, no. 12, pp. 79–81, Nov. 2025.

Issue

Vol. 3 No. 12 (2015): IJCSE December Edition

Section

Review Article

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