**7. A new clustering approach for VLSI circuit partitioning**

The vital problem in VLSI for physical design algorithm is circuit partitioning. In this section concentration is on improving the partitioning technique using data mining approach. This

DBSCAN and clustering algorithm is used for feature Exactor which works on the densities (International Workshop on Text- Based information Retrieval (TIR 05),University of Koblenz-Landau, Germany). It separates the set D into subsets of similar densities. In the best case they can find out the cluster number k routinely and categorize the clusters of random shape and size.The runtime of this algorithms is in magnitude of O(n log(n)) for low-dimensional data (Busch,2005). A density-based cluster algorithm is based on two

In DBSCAN a region is defined as the set of points that lie in the -neighborhood of some point p. if |C| exceeds a given Min Points-threshold Cluster label propagates from p to the other points in C. The complete description of DBSCAN algorithm is provided in (Ester et

In context of the circuit partitining in VLSI design to recognize the subcircuit with minimum interconnection between them, the size of input layer is 4 and output layer is 10. Hence it

Match and choice function for fuzzy ARTMAP in context to circuit partitioning is defined by,


 Winner=max(CFj) (15) Match function which is very much used to find out whether the network must adjust its

If MF j (I) vigilance parameter () then Network is in state of resonance, where is in

The vital problem in VLSI for physical design algorithm is circuit partitioning. In this section concentration is on improving the partitioning technique using data mining approach. This

If MF j (I) vigilance parameter () then Network is in state of mismatch reset.

**7. A new clustering approach for VLSI circuit partitioning** 

(14)


For input vector I and cluster j from DBSCAN algorithm, Choice function given by

Where is small constant about 0.0000001,Wj is top-down weight Winner node is one with highest activation /choice function, that is,

**6.4 Overview of DBSCAN algorithm as a feature exactor** 

al., 1996;Tan et al., 2004;Dagher. I et al., 1999).

outcomes in 2-10 layered Fuzzy ARTMAP model.

**6.5 Simplified fuzzy ARTMAP module** 

learning parameters is given by,

range 0 1.

properties given below (TIR 05,University of Koblenz-Landau, Germany).

1. One is to define a region C D, which forms the basis for density analyses. 2. Another is to propagate density information (the provisional cluster label) of C. section deals with a range of partitioning methodological aspects which predicts to divide the circuit into sub circuits with minimum interconnections between them. This approach considers two clustering algorithms proposed by ( Li & Behjat, 2006) Nearest Neighbor(NN) and Partitioning Around Mediods(PAM) clustering algorithm for dividing the circuits into sub circuits. The experimental results show that PAM clustering algorithm yields better subcircuits than Nearest Neighbour. The experimental results are compared using benchmark data provided by MCNC standard cell placement bench netlists.
