Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimality tests for fixed points of the fuzzy c-means algorithm
Pattern Recognition
Multiple-Way Network Partitioning
IEEE Transactions on Computers
Improving the performance of the Kernighan-Lin and simulated annealing graph bisection algorithms
DAC '89 Proceedings of the 26th ACM/IEEE Design Automation Conference
Clustering and linear placement
DAC '72 Proceedings of the 9th Design Automation Workshop
Efficient partitioning of components
DAC '68 Proceedings of the 5th annual Design Automation Workshop
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
An Improved Min-Cut Algonthm for Partitioning VLSI Networks
IEEE Transactions on Computers
Computer Science Review
Parameter-lite clustering algorithm based on MST and fuzzy similarity merging
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper, a new fuzzy-clustering-based approach is proposed for two-way circuit partitioning. First, a circuit netlist is represented as an undirected edge-weighted graph by a tree net model. Furthermore, the fuzzy memberships and the clustering distance are introduced into the graph. Based on fuzzy c-means clustering, two groups of the fuzzy memberships will be assigned onto all of the vertices in the graph. Finally, according to these fuzzy memberships and the area information of the circuit netlist, the circuit netlist will be partitioned into two smaller netlists with area-balanced constraints. As a result, the proposed fuzzy clustering-based approach is implemented to obtain a better two-way partitioning with area-balanced constraints on circuit benchmarks.