Which of the following is a difference between K means clustering and EM clustering?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

What are the main differences between K means and the Dbscan clustering techniques list two differences?

Difference between K-Means and DBScan Clustering

S.No.	K-means Clustering
1.	Clusters formed are more or less spherical or convex in shape and must have same feature size.
2.	K-means clustering is sensitive to the number of clusters specified.
3.	K-means Clustering is more efficient for large datasets.

What is K means clustering explain with an example?

K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.

When would you use K means cluster & hierarchical cluster technique explain with example?

K Means clustering is found to work well when the structure of the clusters is hyper spherical (like circle in 2D, sphere in 3D). Hierarchical clustering don’t work as well as, k means when the shape of the clusters is hyper spherical.

What is the difference between K-Means and EM?

Answer : Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability). This is where both of these processes differ.

Which of the following are required for K means clustering?

Explanation: K-means requires a number of clusters. Explanation: Hierarchical clustering requires a defined distance as well. 10. K-means is not deterministic and it also consists of number of iterations.

Why K means is better than Dbscan?

The main difference is that they work completely differently and solve different problems. Kmeans is a least-squares optimization, whereas DBSCAN finds density-connected regions. Which technique is appropriate to use depends on your data and objectives.

What’s the difference between Gaussian mixture model and K means?

The first visible difference between K-Means and Gaussian Mixtures is the shape the decision boundaries. GMs are somewhat more flexible and with a covariance matrix ∑ we can make the boundaries elliptical, as opposed to circular boundaries with K-means. Another thing is that GMs is a probabilistic algorithm.

What is the role of K in K means clustering?

You’ll define a target number k, which refers to the number of centroids you need in the dataset. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.

How is K means different from hierarchical clustering?

6. 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).

How do you choose between K means and hierarchical clustering?

K-Means vs Hierarchical

1. If there is a specific number of clusters in the dataset, but the group they belong to is unknown, choose K-means.
2. If the distinguishes are based on prior beliefs, hierarchical clustering should be used to know the number of clusters.
3. With a large number of variables, K-means compute faster.