# K-Means Clustering
The k-means algorithm takes a bunch of unlabeled points and tries to group them into âkâ number of clusters.
The âkâ in k-means denotes the number of clusters you want to have in the end. If k = 5, you will have 5 clusters on the data set.
How it works?
1. Determine K value by Elbow method and specify the number of clusters K
2. Randomly assign each data point to a cluster
3. Determine the cluster centroid coordinates
4. Determine the distances of each data point to the centroids and re-assign each point to the closest cluster centroid based upon minimum distance
5. Calculate cluster centroids again
6. Repeat steps 4 and 5 until we reach global optima where no improvements are possible and no switching of data points from one cluster to other.
#### Quick Run
Import necessary Python packages
```
import numpy
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.cluster import KMeans
```
Define a list containing the distance and the score of similarity in expression profile between the 2 genes
```
xs = [[-53, -200.78],
[117, -267.14],
[57, -163.47],
[16, -190.30],
[11, -220.94],
[85, -193.94],
[16, -182.71],
[15, -180.41],
[-26, -181.73],
[58, -259.87],
[126, -414.53],
[191, -249.57],
[113, -265.28],
[145, -312.99],
[154, -213.83],
[147, -380.85],
[93, -291.13]]
```
Implementation of K-Means Clustering
```
model = KMeans(n_clusters = 2)
model.fit(xs)
model.labels_
colormap = numpy.array(['Red', 'Blue'])
z = plt.scatter([i[0] for i in xs], [i[1] for i in xs], c = colormap[model.labels_]
```
Accuracy estimates Define a list with know answer if the gene pair belongs to the same operon (1) or different operons (0)
```
ys = [1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0]
```
Compare cluster labels with know answer
```
accuracy_score(ys,model.labels_)
```
```
Out[1]: 0.9411764705882353
```
#### Traditional Approach
Import necessary Python packages
```
import numpy
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.cluster import KMeans
```
Define a list containing the distance and the score of similarity in expression profile between the 2 genes
```
xs = [[-53, -200.78],
[117, -267.14],
[57, -163.47],
[16, -190.30],
[11, -220.94],
[85, -193.94],
[16, -182.71],
[15, -180.41],
[-26, -181.73],
[58, -259.87],
[126, -414.53],
[191, -249.57],
[113, -265.28],
[145, -312.99],
[154, -213.83],
[147, -380.85],
[93, -291.13]]
```
Find out the optimal number of clusters using the elbow method
```
Nc = range(1, 10)
kmeans = [KMeans(n_clusters=i) for i in Nc]
kmeans
score = [kmeans[i].fit(xs).score(xs) for i in range(len(kmeans))]
score
plt.plot(Nc,score)
plt.xlabel('Number of Clusters')
plt.ylabel('Score')
plt.title('Elbow Curve')
plt.show()
```
Implementation of K-Means Clustering
```
model = KMeans(n_clusters = 2)
model.fit(xs)
model.labels_
colormap = numpy.array(['Red', 'Blue'])
z = plt.scatter([i[0] for i in xs], [i[1] for i in xs], c = colormap[model.labels_])
```
Accuracy estimates Define a list with know answer if the gene pair belongs to the same operon (1) or different operons (0)
```
ys = [1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0]
```
Compare cluster labels with know answer
```
accuracy_score(ys,model.labels_)
```
```
Out[1]: 0.9411764705882353
```
