k-medoids

An implementation of the k-medoid Partitioning Around Medoids (PAM) algorithm (wikipedia entry)

Example Usage

$3

TypeScript:
`` typescript

import { Cluster, Clusterer } from "k-medoids";

const k = 2;
const myData = [
[1, 2],
[1, 3],
[-1, 2.5],
[0, 0],
[510, 203],
[-100, 120],
];

const clusterer = Clusterer.getInstance(myData, 2);
const clusteredData = clusterer.getClusteredData();
clusteredData

`

JavaScript:
` javascript

const kmeds = require("k-medoids");

const k = 2;
const myData = [
[1, 2],
[1, 3],
[-1, 2.5],
[0, 0],
[510, 203],
[-100, 120],
];

const clusterer = kmeds.Clusterer.getInstance(myData, 2);
const clusteredData = clusterer.getClusteredData();
clusteredData

`

outputs:
` json

[
[
[510,203]
],
[
[1,2],[1,3],[-1,2.5],[0,0],[-100,120]
]
]

`

$3


` typescript

const myFunkyDistanceFn = (a: number[], b: number[]) => {
return Math.abs(a[1] - b[1]);
};

const myClusterer = Clusterer.getInstance(myData, 2, myFunkyDistanceFn);
const data = myClusterer.getClusteredData();
data

`
outputs:
` json
[
[
[510,203],
[-100,120]
],
[
[1,2],
[1,3],
[-1,2.5],
[0,0]
]
]
`

$3

We can cluster any object type as long as we provide a distance function to give the distance between them.

For example with a set of "widgets" like this:
` typescript
const myWidgets = [
{
Name: "DoHickey",
Weight: 10,
},
{
Name: "Thingy",
Weight: 10.5,
},
{
Name: "Whatsit",
Weight: 9.5,
},
{
Name: "Bohemoth",
Weight: 120,
},
{
Name: "Goliath",
Weight: 125,
},
];
`

we might consider items to be similar by weight, and thus:

` typescript
const myWidgetClusterer = Clusterer.getInstance(myWidgets, 2, (a, b) => {
return Math.abs(a.Weight - b.Weight);
});
const groupedWidgets = myWidgetClusterer.getClusteredData();
groupedWidgets
`

gives us:

` json
[
[
{"Name":"Bohemoth","Weight":120},
{"Name":"Goliath","Weight":125}
],
[
{"Name":"DoHickey","Weight":10},
{"Name":"Thingy","Weight":10.5},
{"Name":"Whatsit","Weight":9.5}]
]
]
``