What is the relationship between PCA and K-means clustering?

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.

Why use PCA with K-means?

Principal component analysis (PCA) is a widely used statistical technique for unsuper- vised dimension reduction. K-means clus- tering is a commonly used data clustering for performing unsupervised learning tasks. These results indicate that unsupervised dimension reduction is closely related to unsupervised learning.

What is the difference between hierarchical and k-means clustering?

Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2).

Should PCA be do 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).

Is PCA required before clustering?

In short, using PCA before K-means clustering reduces dimensions and decrease computation cost. On the other hand, its performance depends on the distribution of a data set and the correlation of features.So if you need to cluster data based on many features, using PCA before clustering is very reasonable.

Why K means is not a hierarchical clustering?

K-Means is that it needs us to pre-enter the number of clusters (K) but Hierarchical clustering has no such requirements. The algorithm on itself deduces the optimum number of cluster and displays it form of dendrogram. K-Means need circular data, while Hierarchical clustering has no such requirement.