Balanced clustering
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Balanced clustering
Balanced clustering is a special case of clustering, where in the strictest.sense, the cluster sizes are constrained to or , where is the number of points and 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 inbalance 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.
Balanced clustering
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