cluster

Documentation

Overview

Package cluster contains clustering algorithms.

Index

• func DBSCAN(data [][]int, eps float64, mp int) []map[int][]int
• func DBSCANData(data []map[int]int, eps float64, mp int) [][]map[int]int
• func KMeans(k int, data [][]int) []map[int][]int
• func KMeansData(k int, data []map[int]int) []map[int][]int
• func KMeansDataI(k int, data []map[int]int) [][]map[int]int
• func KMeansF(k int, D [][]float64, sc map[int]int) []map[int][]float64
• func KMeansFData(k int, D []map[int]int) []map[int][]float64
• func KMeansI(k int, D [][]float64) []int
• func KMeansMuesli(k int, M [][]int, sc map[int]int) []map[int][]int
• func KMeansMuesliF(k int, D [][]float64, sc map[int]int) []map[int][]float64
• func KMedoid(k int, data [][]int) []map[int][]int
• func KMode(k int, data [][]int) []map[int][]int
• func OPTICS(data [][]int, eps float64, mp int) []map[int][]int
• func OldKMeans(k int, data [][]int) []map[int][]int
• func OldKMeansF(k int, D [][]float64, F metrics.MetricF) []map[int][]float64

Functions

func DBSCAN

func DBSCAN(data [][]int, eps float64, mp int) []map[int][]int

DBSCAN Density-based spatial clustering of applications with noise. Parameters:

• data is data matrix;
• eps is epsilon maximum distance between density core points;
• mp is minimum number of points to be considered core point.

func DBSCANData

func DBSCANData(data []map[int]int, eps float64, mp int) [][]map[int]int

func KMeans

func KMeans(k int, data [][]int) []map[int][]int

func KMeansData

func KMeansData(k int, data []map[int]int) []map[int][]int

func KMeansDataI

func KMeansDataI(k int, data []map[int]int) [][]map[int]int

func KMeansF

func KMeansF(k int, D [][]float64, sc map[int]int) []map[int][]float64

func KMeansFData

func KMeansFData(k int, D []map[int]int) []map[int][]float64

func KMeansI

func KMeansI(k int, D [][]float64) []int

func KMeansMuesli

func KMeansMuesli(k int, M [][]int, sc map[int]int) []map[int][]int

func KMeansMuesliF

func KMeansMuesliF(k int, D [][]float64, sc map[int]int) []map[int][]float64

func KMedoid

func KMedoid(k int, data [][]int) []map[int][]int

func KMode

func KMode(k int, data [][]int) []map[int][]int

func OPTICS

func OPTICS(data [][]int, eps float64, mp int) []map[int][]int

OPTICS - Ordering points to identify the clustering structure (OPTICS). OPTICS is similar to DBSCAN with the exception that instead of an epsilon to bound the distance between points, OPTICS replaces that epsilon with a new epsilon that upper bounds the maximum possible epsilon a DBSCAN would take. Parameters:

• data is data matrix;
• eps is a maximum distance between density core points upper bound;
• mp is minimum number of points to be considered core point.

func OldKMeans

func OldKMeans(k int, data [][]int) []map[int][]int

func OldKMeansF

func OldKMeansF(k int, D [][]float64, F metrics.MetricF) []map[int][]float64