Enum kodama::Method

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

Single

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.

Complete

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.

Average

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.

Weighted

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.

Ward

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.

Centroid

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.

Median

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.

Methods

impl Method

pub fn into_method_chain(self) -> Option<MethodChain>

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 Clone for Method

fn clone(&self) -> Method

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for Method

impl Debug for Method

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Eq for Method

impl PartialEq for Method

fn eq(&self, other: &Method) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl FromStr for Method

type Err = Error

The associated error which can be returned from parsing.

fn from_str(s: &str) -> Result<Method>

Parses a string s to return a value of this type.