Table of Contents
- 1 Do I need to do PCA before K-means?
- 2 Why do we do PCA before clustering?
- 3 How is k-means clustering similar to PCA?
- 4 What are the methods in getting the value of K in K-means clustering?
- 5 Should PCA be applied before or after k-means clustering algorithm?
- 6 How many principal components should I use for PCA?
Do I need to do PCA before K-means?
Note that the k-mean clustering algorithm is typically slow and depends in the number of data points and features in your data set. In summary, it wouldn’t hurt to apply PCA before you apply a k-means algorithm.
How do you choose K in PCA?
1 Answer
- Run PCA for the largest acceptable K on training set,
- Plot, or prepare (k, variance) on validation set,
- Select the k that gives the minimum acceptable variance, e.g. 90\% or 99\%.
Why do we do PCA before clustering?
By doing PCA you are retaining all the important information. If your data exhibits clustering, this will be generally revealed after your PCA analysis: by retaining only the components with the highest variance, the clusters will be likely more visibile (as they are most spread out).
Does PCA improve clustering?
PCA is sometimes applied to reduce the dimensionality of the dataset prior to clustering. However, Yeung & Ruzzo (2000) showed that clustering with the PC’s instead of the original variables does not necessarily improve cluster quality.
How is k-means clustering similar to PCA?
K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.
Is K-means supervised or unsupervised?
K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.
What are the methods in getting the value of K in K-means clustering?
There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.
What is the difference between PCA and k-means?
K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.
Should PCA be applied before or after k-means clustering algorithm?
It is a common practice to apply PCA (principal component analysis) before a clustering algorithm (such as k-means). It is believed that it improves the clustering results in practice (noise reduction).
What is the relationship between k-means clustering and principal component analysis?
However, as explained in the Ding & He 2004 paper K-means Clustering via Principal Component Analysis, there is a deep connection between them. The intuition is that PCA seeks to represent all $n$data vectors as linear combinations of a small number of eigenvectors, and does it to minimize the mean-squared reconstruction error.
How many principal components should I use for PCA?
A rule of thumb is to preserve around 80 \% of the variance. So, in this instance, we decide to keep 3 components. As a third step, we perform PCA with the chosen number of components. For our data set, that means 3 principal components: