R/dependency_type_fhm.R
dependency_type_fhm.Rd
Computes the dependencies based on the approach known as Flexible Heuristics Miner.
dependency_type_fhm(
threshold_dependency = 0.9,
threshold_l1 = threshold_dependency,
threshold_l2 = threshold_dependency,
threshold_frequency = 0,
all_connected = FALSE,
endpoints_connected = FALSE
)
A dependency threshold, usually in the interval [0,1]
, filtering out dependencies below the threshold.
A dependency threshold, usually in the interval [0,1]
, filtering out self-loop dependencies below the threshold.
A dependency threshold, usually in the interval [0,1]
, filtering out length-two loop dependencies below the threshold.
An absolute frequency threshold filtering dependencies which are observed infrequently.
If TRUE
the best antecedent and consequent (as determined by the dependency measure) are going to be added regardless of the threshold
value.
If TRUE
the start/end activity is added as antecedent/consequent when an activity would not be connected according to the threshold
value.
A dependency type.
A. J. M. M. Weijters and J. T. S. Ribeiro, "Flexible Heuristics Miner (FHM)," 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 2011, pp. 310-317. doi: 10.1109/CIDM.2011.5949453
dependency_matrix(L_heur_1,
dependency_type = dependency_type_fhm(all_connected = TRUE))
#> consequent
#> antecedent End Start a b c d e
#> End 0.0000000 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> Start 0.0000000 0 0.9756098 0.0000000 0.0000000 0.0000000 0.0000000
#> a 0.0000000 0 0.0000000 0.9166667 0.9166667 0.9285714 0.0000000
#> b 0.0000000 0 0.0000000 0.0000000 0.0000000 0.0000000 0.9166667
#> c 0.0000000 0 0.0000000 0.0000000 0.0000000 0.0000000 0.9166667
#> d 0.0000000 0 0.0000000 0.0000000 0.0000000 0.0000000 0.9285714
#> e 0.9756098 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> attr(,"class")
#> [1] "dependency_matrix" "matrix" "array"