V-2 Clustering with Kmeans: Normalize our data, Does it improve it? | Python | Unsupervised learning
Are you ready to learn about Kmeans? Does normalization of our data improves k-means performance?
#python #k-means #kmeans
In this super chapter, we'll cover the discovery of clusters or groups through the partitioning algorithm K-means with python and the JUPYTER NOTEBOOK. Pandas libraries for data manipulation, matplotlib for creation of graphics, sklearn for calling the clustering function KMeans.
Video #2:
What is Kmeans?
Why should we normalize our data before clustering?
What is the difference between scaling and normalization?
How normalization works?
Formulas for normalization and scale: min, max, normal distribution, standard deviation
Graphics of cluster with scatterplots: No normalized data vrs normalized data
Discussion of results of No normalized data and clustering vrs normalized data and clusters
Video #1: • V-1: Clustering with Kmeans in Python: Skl...
How the algorithm Kmeans works?
Characteristics of K-means: advantages and disadvantages
Centroids and number of clusters (groups)
Creating and using synthetic data to test the results
Clustering with kmeans using library sklearn
How kmeans deals with outliers?
Cluster new points
Graphics of cluster with scatterplots
Hierarchical clustering with python
Video Chapter 1: • V-1 Hierarchical clustering with Python: s...
clustering in R
• Hierarchical Clustering | Agrupamiento jer...
Any comments or suggestions are welcome.
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