https://github.com/ekojs/ejs_k-means
Penerapan K-Means Clustering pada Javascript
https://github.com/ekojs/ejs_k-means
clustering clustering-algorithm data-mining-algorithms data-science k-means
Last synced: 2 months ago
JSON representation
Penerapan K-Means Clustering pada Javascript
- Host: GitHub
- URL: https://github.com/ekojs/ejs_k-means
- Owner: ekojs
- License: mit
- Created: 2017-02-09T12:04:41.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-03-16T09:46:05.000Z (about 8 years ago)
- Last Synced: 2025-01-08T19:39:47.497Z (4 months ago)
- Topics: clustering, clustering-algorithm, data-mining-algorithms, data-science, k-means
- Language: HTML
- Size: 579 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## K-Means Clustering Algorithm
### Penerapan K-Means Clustering pada Javascript
### Cara melakukan Test Training pada [`ejs_kmeans.js`](https://github.com/ekojs/machine_learning/blob/master/unsupervised/ejs_kmeans.js)
#### 1 - Create file tes.js pada folder yg sama dengan file ejs_k-means.js
```javascript
var ejs_kmeans = require('./ejs_kmeans');
function TestData(samples,centroid){
console.log('Samples Data : %s \n','('+samples.join(') (')+')');
var k_means = new ejs_kmeans.k_mean_cluster(samples);
k_means.initialize(centroid);
k_means.calculate();
console.log(k_means.result().replace(/
/g,"\n").replace(/ /g,' ').replace(/<\/?strong>/g,''));
}
TestData([[5.09,5.80], [3.24,5.90], [1.68,4.90], [1.00,3.17], [1.48,1.38], [2.91,0.20], [4.76,0.10], [6.32,1.10], [7.00,2.83], [6.52,4.62]],[[1.48,1.38],[4.76,0.10]]);
//TestData([[5.09,5.80], [3.24,5.90], [1.68,4.90], [1.00,3.17], [1.48,1.38], [2.91,0.20], [4.76,0.10], [6.32,1.10], [7.00,2.83], [6.52,4.62]],[[5.09,5.80], [3.24,5.90]]);
//TestData([[1.0,1.0],[1.5,2.0],[3.0,4.0],[5.0,7.0],[3.5,5.0],[4.5,5.0],[3.5,4.5]],[[1,1],[5,7]]);
//TestData([[1,1],[2,1],[4,3],[5,4]],[[1,1],[2,1]]);
//TestData([[1,1,2],[2,1,3],[4,3,2],[5,4,4],[4,4,4]],[[1,1,2],[2,1,3]]);
//TestData([[5.09,5.80], [3.24,5.90], [1.68,4.90], [1.00,3.17], [1.48,1.38], [2.91,0.20], [4.76,0.10], [6.32,1.10], [7.00,2.83], [6.52,4.62]],[[5.09,5.80], [3.24,5.90], [1.68,4.90]]);
```
#### 3 - Eksekusi langsung
```javascript
node tes.js
```
#### 4 - Hasilnya kurang lebih akan seperti ini
```javascript
Samples Data : (5.09,5.8) (3.24,5.9) (1.68,4.9) (1,3.17) (1.48,1.38) (2.91,0.2) (4.76,0.1) (6.32,1.1) (7,2.83) (6.52,4.62)
Centroids initialized at:
(1.48,1.38)
(4.76,0.1)
Cluster 0 includes:
(5.09,5.8)
(3.24,5.9)
(1.68,4.9)
(1,3.17)
(1.48,1.38)
Cluster 1 includes:
(2.91,0.2)
(4.76,0.1)
(6.32,1.1)
(7,2.83)
(6.52,4.62)
Centroids finalized at:
(2.498,4.23)
(5.502000000000001,1.7700000000000002)
```
#### Note:
Baca artikel pada [`K-Means Clustering Algorithm`](http://ekojunaidisalam.com/2017/02/09/k-means-clustering-algorithm/)
Simulasi program di [`sini`](https://ekojs.github.io/ejs_k-means/)