VLSI Cell Partitioning Using Data Mining Approaches

Authors

  • Nayak S Ph. D Scholar, P.G. Dep. of CSA, Utkal University, Vani Vihar, Odisha, India
  • Panda M P.G. Dep. of CSA, Utkal University, Vani Vihar, Odisha, India

DOI:

https://doi.org/10.26438/ijcse/v6i8.10191027

Keywords:

K-means algorithms, K-nearest neighbor, Fuzzy c-means, The Support Vector Machines, Partitioning, and Data mining

Abstract

Theoretical studies on various cell partitioning methods are lucidly presented in the current research pertaining to design and development of VLSI circuits. Owing to the difficulties in designing complex VLSI systems, it is extremely crucial to partition the large circuit into tiny logic blocks to reduce time complexity, space complexity and power consumption. To envisage the same, this communication scrutinizes a heuristic technique by using various data mining algorithms such as Kmeans algorithms, K-Nearest Neighbor (K-NN), Fuzzy c-means and Support Vector Machine (SVM) for resolution of complexity in VLSI circuits, where K-NN and SVM are employed for classification purpose and Fuzzy c-means and K-means methodologies are deployed for clustering purpose. The upshot of the research revealed that K-NN and Fuzzy c-means methods bestow optimum result pertaining to VLSI cell partitioning.

References

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Published

2025-11-15
CITATION
DOI: 10.26438/ijcse/v6i8.10191027
Published: 2025-11-15

How to Cite

[1]
S. Nayak and M. Panda, “VLSI Cell Partitioning Using Data Mining Approaches”, Int. J. Comp. Sci. Eng., vol. 6, no. 8, pp. 1019–1027, Nov. 2025.

Issue

Vol. 6 No. 8 (2018): IJCSE August Edition

Section

Research Article

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