Class MedianAgglomerativeClustering

_d

_metric

_root

_lanceWilliamsUpdater

• _lanceWilliamsUpdater(
      ala: any,
      alb: any,
      bt: any,
      gm: any,
  ): (ka: any, kb: any, ab: any) => number

• Parameters

  • ala: any
  • alb: any
  • bt: any
  • gm: any

  Returns (ka: any, kb: any, ab: any) => number

_mean

• _mean(d: any): number[]

• Parameters

  • d: any

  Returns number[]

Abstractdistance

• distance(c1: AgglomerativeClusterNode, c2: AgglomerativeClusterNode): number

• Returns a distance between two nodes.

  Parameters

  • c1: AgglomerativeClusterNode

    Node
  • c2: AgglomerativeClusterNode

    Node

  Returns number

  Distance

fit

• fit(points: number[][]): void

• Fit model parameters.

  Parameters

  • points: number[][]

    Training data

  Returns void

getClusters

• getClusters(number: number): AgglomerativeClusterNode[]

• Returns the specified number of clusters.

  Parameters

  • number: number

    Number of clusters

  Returns AgglomerativeClusterNode[]

  Cluster nodes

predict

• predict(k: number): number[]

• Returns predicted categories.

  Parameters

  • k: number

    Number of clusters

  Returns number[]

  Predicted values

Abstractupdate

• update(
      ca: number,
      cb: number,
      ck: number,
      ka: number,
      kb: number,
      ab: number,
  ): number

• Returns new distance.

  Parameters

  • ca: number

    Number of datas in a merging node A
  • cb: number

    Number of datas in a merging node B
  • ck: number

    Number of datas in a current node
  • ka: number

    Distance between node A and current node
  • kb: number

    Distance between node B and current node
  • ab: number

    Distance between node A and node B

  Returns number

  New distance between current node and merged node