Similarity-based clustering method (SCM)

Similarity-Based Clustering Method (SCM)
- Python for Integrated Circuits -
- An Online Book -

Python for Integrated Circuits http://www.globalsino.com/ICs/

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

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The final SCA result can have n final cluster centers, namely, the final states of all n data points. In this case, the n final cluster centers are centralized to N peaks of the SCM objective function. To find the optimal N value and to classify the data set into these N clusters, agglomerative hierarchical clustering (AHC) technique can be applied, so that the n final cluster centers can be processed. There are many methods for processing AHC: [1]
          i) The single linkage method.
          ii) The complete linkage method.
          iii) Ward’s method.

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[1] M.-S. Yang and K.-L. Wu, “A similarity-based robust clustering method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 4, pp. 434–448, Apr. 2004.

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