Enum linfa_hierarchical::Method [−][src]
pub enum Method {
Single,
Complete,
Average,
Weighted,
Ward,
Centroid,
Median,
}A method for computing the dissimilarities between clusters.
The method selected dictates how the dissimilarities are computed whenever
a new cluster is formed. In particular, when clusters a and b are
merged into a new cluster ab, then the pairwise dissimilarity between
ab and every other cluster is computed using one of the methods variants
in this type.
Variants
Assigns the minimum dissimilarity between all pairs of observations.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
min(d[ab, x] for ab in AB for x in X)
where ab and x correspond to all observations in AB and X,
respectively.
Assigns the maximum dissimilarity between all pairs of observations.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
max(d[ab, x] for ab in AB for x in X)
where ab and x correspond to all observations in AB and X,
respectively.
Assigns the average dissimilarity between all pairs of observations.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
sum(d[ab, x] for ab in AB for x in X) / (|AB| * |X|)
where ab and x correspond to all observations in AB and X,
respectively, and |AB| and |X| correspond to the total number of
observations in AB and X, respectively.
Assigns the weighted dissimilarity between clusters.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
0.5 * (d(A, X) + d(B, X))
where A and B correspond to the clusters that merged to create
AB.
Assigns the Ward dissimilarity between clusters.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
let t1 = d(A, X)^2 * (|A| + |X|);
let t2 = d(B, X)^2 * (|B| + |X|);
let t3 = d(A, B)^2 * |X|;
let T = |A| + |B| + |X|;
sqrt(t1/T + t2/T + t3/T)
where A and B correspond to the clusters that merged to create
AB.
Assigns the centroid dissimilarity between clusters.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
let t1 = |A| * d(A, X) + |B| * d(B, X));
let t2 = |A| * |B| * d(A, B);
let size = |A| + |B|;
sqrt(t1/size + t2/size^2)
where A and B correspond to the clusters that merged to create
AB.
Assigns the median dissimilarity between clusters.
Specifically, if AB is a newly merged cluster and X is every other
cluster, then the pairwise dissimilarity between AB and X is
computed by
sqrt(d(A, X)/2 + d(B, X)/2 - d(A, B)/4)
where A and B correspond to the clusters that merged to create
AB.
Implementations
impl Method[src]
impl Method[src]pub fn into_method_chain(self) -> Option<MethodChain>[src]
Convert this linkage method into a nearest neighbor chain method.
More specifically, if this method is a method that the nnchain
algorithm can compute, then this returns the corresponding
MethodChain value. Otherwise, this returns None.
Trait Implementations
impl StructuralEq for Method[src]
impl StructuralEq for Method[src]impl StructuralPartialEq for Method[src]
impl StructuralPartialEq for Method[src]Auto Trait Implementations
impl RefUnwindSafe for Method
impl RefUnwindSafe for Methodimpl UnwindSafe for Method
impl UnwindSafe for Method