EntropyOfDataField: Entropy Of a Data Field [Wang et al., 2011].

In FCPS: Fundamental Clustering Problems Suite

Entropy Of a Data Field [Wang et al., 2011].

Description

Calculates the Potential Entropy Of a Data Field for a given ranges of impact factors sigma

Usage

EntropyOfDataField(Data, 

sigmarange = c(0.01, 0.1, 0.5, 1, 2, 5, 8, 10, 100)

, PlotIt = FALSE)

Arguments

Data	[1:n,1:d] data matrix
sigmarange	Numeric vector [1:s] of relevant sigmas
PlotIt	FALSE: disable plot, TRUE: Plot with upper boundary of H after [Wang et al., 2011].

Details

In theory there should be a curve with a clear minimum of Entropy [Wang et al.,2011]. Then the choice for the impact factor sigma is the minimum of the entropy to define the correct data field. It follows, that the influence radius is 3/sqrt(2)*sigma (3B rule of gaussian distribution) for clustering algorithms like density peak clustering [Wang et al.,2011].

Value

[1:s] named vector of the Entropy of data field. The names are the impact factor sigma.

Author(s)

Michael Thrun

References

[Wang et al., 2015] Wang, S., Wang, D., Li, C., & Li, Y.: Comment on" Clustering by fast search and find of density peaks", arXiv preprint arXiv:1501.04267, 2015.

[Wang et al., 2011] Wang, S., Gan, W., Li, D., & Li, D.: Data field for hierarchical clustering, International Journal of Data Warehousing and Mining (IJDWM), Vol. 7(4), pp. 43-63. 2011.

Examples

data(Hepta)
H=EntropyOfDataField(Hepta$Data,PlotIt=FALSE)
Sigmamin=names(H)[which.min(H)]
Dc=3/sqrt(2)*as.numeric(names(H)[which.min(H)])

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.