Today’s Lecture:-
K-Means Clustering:-
- K-means is a partitioning method that aims to divide a dataset into K distinct, non-overlapping clusters. It is a centroid-based approach, where the data points are assigned to the cluster with the nearest centroid.
- To have an improved comprehension of medoids, we may compare them to the core points in K-Means clustering.
- The relationship between averages and middle values in a list is comparable to the relationship between medoids and center points.
- But it’s important to remember that while averages and central points might not always be real data points, medians and middle values are.
- The primary distinction between K-Means and K-Medoids is how they arrange the data.
- While K-Means arranges information according to the distances between data points and central points, K-Medoids arranges data according to the distances to medoids.
- Since K-Medoids do not depend on center points, they are more robust and resistant to the effects of unusual data, making them an excellent choice for managing outliers.