clustering

Documentation

Overview

This package implements clustering algorithms

Index

• func BitCount(n *big.Int) int
• type ClusterMap
  • func DBSCAN(inst base.FixedDataGrid, params DBSCANParameters) (ClusterMap, error)
  • func (ref ClusterMap) Equals(other ClusterMap) (bool, error)
  • func (ref ClusterMap) Invert() (map[int]int, error)
• type ClusterParameters
• type DBSCANParameters

Functions

func BitCount

func BitCount(n *big.Int) int

How many bits?

Types

type ClusterMap

type ClusterMap map[int][]int

ClusterMap contains the cluster identifier as a key, followed by a vector of point indices that cluster contains.

func DBSCAN

func DBSCAN(inst base.FixedDataGrid, params DBSCANParameters) (ClusterMap, error)

DBSCAN clusters inst using the parameters allowed in and produces a ClusterId->[RowId] map

func (ClusterMap) Equals

func (ref ClusterMap) Equals(other ClusterMap) (bool, error)

Equals checks whether a bijection exists between two ClusterMaps (i.e. the clusters in one can be re-labelled to become the clusters of another)

func (ClusterMap) Invert

func (ref ClusterMap) Invert() (map[int]int, error)

Invert returns an alternative form of cluster map where the key represents the point index and the value represents the cluster index it's assigned to

type ClusterParameters

type ClusterParameters struct {
	// Attributes represents the set of Attributes which
	// can be used for clustering
	Attributes []base.Attribute

	// Metric is used to compute pairwise distance
	Metric pairwise.PairwiseDistanceFunc
}

type DBSCANParameters

type DBSCANParameters struct {
	ClusterParameters

	// Eps represents the "reachability", or the maximum
	// distance any point can be before being considered for
	// inclusion.
	Eps float64

	// MinCount represents how many points need to be
	// in a cluster before it is considered one.
	MinCount int
}

DBSCANParameters describes the parameters of the density-based clustering algorithm DBSCAN