Balanced clustering

Balanced clustering

Balanced clustering is a special case of clustering, where in the strictest sense, the cluster sizes are constrained to $\lfloor {n \over k}\rfloor$ or $\lceil {n \over k}\rceil$ , where $n$ is the number of points and $k$ is the number of clusters.^[1] This type of balanced clustering is called balance-constrained clustering. Typical algorithm is Balanced k-Means, which minimizes mean square error (MSE). There is also another type of balanced clustering, it is called balance-driven clustering. In it the cost function is two-objective that minimizes both imbalance and MSE. Typical cost functions are Ratio cut^[2] and Ncut.^[3]

Software

There exists implementations for Balanced k-Means^[4] and Ncut^[5]

References

↑ M. I. Malinen and P. Fränti (August 2014). "Balanced k-Means for Clustering". Joint Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2014), LNCS 8621.
↑ L. Hagen and A. B. Kahng (1992). "New spectral methods for ratio cut partitioning and clustering". IEEE Transactions on Computer-Aided Design.
↑ J. Shi and J. Malik (2000). "Normalized cuts and image segmentation". IEEE Transactions on Pattern Analysis and Machine Intelligence.
↑ M. I. Malinen and P. Fränti. "Balanced k-Means implementation". University of Eastern Finland.
↑ T. Cour, S. Yu and J. Shi. "Ncut implementation". University of Pennsylvania.

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