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kmeans clustering

Ayoni02 answered on June 11, 2022 Popularity 6/10 Helpfulness 1/10

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kmeans clustering

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# Kmeans clustering
from sklearn.cluster import KMeans
KM = KMeans(n_clusters=4, random_state=42)
ans = KM.fit(df1.iloc[:,1:-1])
ans.labels_

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Contributed on Sep 01 2024

Ayoni02

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k-means clustering

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- Partition-based clustering that produces sphere-like clusters
- Divides data into non-overlapping subsets with distance metric
- Examples in a clusters share similar pattern
	(Can be found by minimizing intra-cluster distances)
- Examples in different clusters are different
	(Can be found by maximizing inter-cluster distances)
- see : https://dribbble.com/shots/20642101-k-means-clustering-objective

Process:
1. Randomly select 3 points/centroids for clustering
2. Calculate distance of each datapoint from the centroids. The datapoint
	belongs to the centroid closest to it.
3. Calculate SSE. The goal is to reduce the SSE (sum of squared errors)
4. Update centroids by estimating the mean of each cluster
5. Iterate from step 2 unless the value converges for centroids (centroids no
	longer move much)

Issue: Result may or may not be the best outcome (may converge to local optima)
Resolve: Run the algorithm multiple times with different random starting points

Evaluation metric:
	1. Compare with ground truth (If available. Normally no ground truth exists)
    2. Cluster error  : (How losely data form a cluster)
    	a. Average distance between data points 
        b. Average distance between centroid and datapoints
Choosing best K: (The elbow method) 
	- run the clustering algorithm for different values of k and choose the k
    	that gives the best accuracy and forms an elbow point in visualization.

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Contributed on Feb 13 2023

Innocent Iguana

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k-means clustering

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# Import the kmeans and vq functions
from scipy.cluster.vq import kmeans, vq

# Set up a random seed in numpy
random.seed([1000,2000])

# Fit the data into a k-means algorithm
cluster_centers,distortion = kmeans(fifa[['scaled_def', 'scaled_phy']], 3)

# Assign cluster labels
fifa['cluster_labels'],distortion_list = vq(fifa[['scaled_def', 'scaled_phy']], cluster_centers)

# Display cluster centers 
print(fifa[['scaled_def', 'scaled_phy', 'cluster_labels']].groupby('cluster_labels').mean())

# Create a scatter plot through seaborn
sns.scatterplot(x='scaled_def', y='scaled_phy', hue='cluster_labels', data=fifa)
plt.show()

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Contributed on Jun 11 2022

josh.ipynb

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kmeans clustering

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