AgglomerationType: List of SHC Agglomeration Templates

In dkrleza/SHClus: Statistical Hierarchical Clustering

Description

Described in [1]. The following templates are available:

• NormalAgglomeration - theta = 3.2, virtualVariance = 1.0

• AggresiveAgglomeration - theta = 3.5, virtualVariance = 1.2

• RelaxedAgglomeration - theta = 2.9, virtualVariance = 0.8

Author(s)

Dalibor Krleža

References

[1] Krleža D, Vrdoljak B, and Brčić M, Statistical hierarchical clustering algorithm for outlier detection in evolving data streams, Machine Learning, Sep. 2020

Examples

set.seed(0)
d <- DSD_Gaussians(k = 3, d = 2, outliers = 5, separation_type = "Mahalanobis", 
    separation = 6, space_limit = c(0, 50), variance_limit = 2,
    outlier_options = list(outlier_horizon = 10000,outlier_virtual_variance = 2))
c <- DSC_SHC.behavioral(2, aggloType = AgglomerationType$AggresiveAgglomeration, decaySpeed = 0, 
      sigmaIndex = TRUE)
evaluate(c, d, n = 10000, type = "macro", measure = c("crand", "outlierjaccard"))

set.seed(0)
d <- DSD_Gaussians(k = 3, d = 2, outliers = 5, separation_type = "Mahalanobis", 
    separation = 6, space_limit = c(0, 50), variance_limit = 2,
    outlier_options = list(outlier_horizon = 10000,outlier_virtual_variance = 2))
c <- DSC_SHC.behavioral(2, aggloType = AgglomerationType$RelaxedAgglomeration, decaySpeed = 0, 
      sigmaIndex = TRUE)
evaluate(c, d, n = 10000, type = "macro", measure = c("crand", "outlierjaccard"))

dkrleza/SHClus documentation built on Feb. 25, 2021, 10:30 p.m.