vanDongen_fast: Van Dongen Criterion

In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series

Description

Calculates the index of van Dongen (2000) to compare two cluster assignment vectors (external cluster validation). It is a value in [0,2n), lower values indicating more similarity (it matches each cluster of one assignment to the most similar cluster in the other assignment and counts mismatches). Optionally, the index can be normalized to [0,1] as proposed by Wu, Xiong and Chen (2009). After normalization, we take 1 - normalizedValue so that higher values indicate better clustering quality (as it is for indices like Rand, Fowlkes-Mallows).

Usage

vanDongen_fast(assignments1, assignments2, normalizeAndInvert = FALSE)

Arguments

assignments1	Integer vector of cluster assignments containing only values from 1 to k with k = number of clusters (code depends on this!).
assignments2	Integer vector of cluster assignments containing only values from 1 to k with k = number of clusters.
normalizeAndInvert	Should the van Dongen criterion be normalized to [0,1] and inverted such that high values indicate similar clusterings?

Value

The van Dongen criterion as double (in [0,2n) without normalization and [0,1] else).

References

Van Dongen, S. (2000). Performance criteria for graph clustering and markov cluster experiments. National Research Institute for Mathematics and Computer Science. Amsterdam.

Wu, J., Xiong, H. & Chen, J. (2009). Adapting the right measures for k-means clustering. In Proceedings of the 15th acm sigkdd international conference on knowledge discovery and data mining (pp. 877-886). ACM.

See Also

Other External Cluster Validity Indices: conditionalEntropy_fast, fowlkesMallows_fast, pairCVIParameters_fast, phi_fast, purity_fast, randIndex_fast

Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.