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This article introduces the relevant knowledge of "how to use Golang basic data structure and algorithm k-means clustering algorithm". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

K-means clustering algorithm

Clustering is the operation of dividing "similar" data into a group when entering multiple data. K-means algorithm is one of the clustering algorithms. Firstly, k points are randomly selected as the center point of the cluster, and then the operations of "dividing the data into the corresponding cluster" and "moving the center point to the position of the center of gravity" are repeated until the center point no longer changes. In the k-means algorithm, with the continuous repetition of the operation, the position of the center point must converge somewhere, which has been proved on the mathematical level. Excerpt from [Japan] Ishida Baohui; Miyazaki Shoichi

Scene

There is a sudden outbreak of COVID-19 epidemic in a certain place, and epidemic prevention urgently needs to find out the possible source of the disease according to the distribution of cases.

First of all, enter the coordinates of the case distribution into the system.

Then, according to k-means algorithm, clustering is carried out according to k from 1 to 3.

The central point of clustering may be the source of the disease.

Process flow

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Given several samples and the sample distance calculator, k sample center points need to be solved.

Firstly, k points are randomly selected from the sample as the central point.

Loop each sample

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Calculate the distance from the sample point to the k center point respectively

Judge the center point with the smallest distance from the sample point

A cluster that divides the sample into the minimum center point.

Calculate the center point of each cluster as the new center point

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Each sample in a cyclic cluster

Calculate the sum of the distances from the sample to the other samples in this cluster

The distance from other samples and the smallest point is the new central point.

Repeat 3-4 until the center point no longer changes. Calculation complete.

Design

IPoint: sample point interface, which is actually an empty interface

IDistanceCalculator: distance Calculator Interface

IClassifier: classifier interface, clustering samples into k and returning k central points

TPerson: case sample point, implementation of IPoint interface, including XBI y coordinates

TPersonDistanceCalculator: case distance calculator, which calculates the straight line distance between two points.

TKMeansClassifier: k-means aggregator to implement the IClassifier interface.

Unit testing

K_means_test.go

Package others import (km "learning/gooop/others/k_means"strings"testing") func Test_KMeans (t * testing.T) {/ / create sample points samples: = [] km.IPoint {km.NewPerson (2,11), km.NewPerson (2,8), km.NewPerson (2,6), km.NewPerson (3,12), km.NewPerson (3) 10), km.NewPerson (4,7), km.NewPerson (4,3), km.NewPerson (5,11), km.NewPerson (5,9), km.NewPerson (5,2), km.NewPerson (7,9), km.NewPerson (7,6), km.NewPerson (7,3), km.NewPerson (8,12) Km.NewPerson (9,3), km.NewPerson (9,5), km.NewPerson (9,10), km.NewPerson (10,3), km.NewPerson (10,6), km.NewPerson (10,12), km.NewPerson (11,9),} fnPoints2String: = func (points [] km.IPoint) string {items: = make ([] string) Len (points)) for iJournal it: = range points {items [I] = it.String ()} return strings.Join (items, ")} for KRV 1 K

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