What are the limitations of the K means clustering algorithm?
Table of Contents
- 1 What are the limitations of the K means clustering algorithm?
- 2 Which statement is not true about K means clustering?
- 3 What is the difference between k-means and K Medoids?
- 4 What is the main difference between K-means and hierarchical clustering?
- 5 What are different similarities between K-means and KNN algorithm?
- 6 Which statement is not true about K-means clustering Mcq?
What are the limitations of the K means clustering algorithm?
The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.
Which statement is not true about K means clustering?
| Q. | Which Statement is not true statement. |
|---|---|
| A. | k-means clustering is a linear clustering algorithm. |
| B. | k-means clustering aims to partition n observations into k clusters |
| C. | k-nearest neighbor is same as k-means |
| D. | k-means is sensitive to outlier |
What is the difference between k-means and K Medoids?
K-means attempts to minimize the total squared error, while k-medoids minimizes the sum of dissimilarities between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k -means algorithm, k -medoids chooses datapoints as centers ( medoids or exemplars).
How is Knn different from K-means clustering?
K-means clustering represents an unsupervised algorithm, mainly used for clustering, while KNN is a supervised learning algorithm used for classification. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.
What is the difference between K-means and K Medoids?
What is the main difference between K-means and hierarchical clustering?
Difference between K means and Hierarchical Clustering
| k-means Clustering | Hierarchical Clustering |
|---|---|
| One can use median or mean as a cluster centre to represent each cluster. | Agglomerative methods begin with ‘n’ clusters and sequentially combine similar clusters until only one cluster is obtained. |
What are different similarities between K-means and KNN algorithm?
KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.