## What is the difference between K-means clustering and Gaussian mixture models?

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.

Which method can be used to find K in K-means clustering?

elbow method
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.

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### How do you use K-means clustering for classification?

KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes.

How can I combine content based recommender system with K-means clustering?

The first thing to do is to cluster your dataset based on some features in them that are appropriate for the type of recommendation you want to generate by the use of k-means clustering algorithm.

#### When to use K-means vs Gaussian mixture?

Gaussian mixture models can be used to cluster unlabeled data in much the same way as k-means. There are, however, a couple of advantages to using Gaussian mixture models over k-means. First and foremost, k-means does not account for variance. In contrast, Gaussian mixture models can handle even very oblong clusters.

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Why use K-means over GMM?

k-means only considers the mean to update the centroid while GMM takes into account the mean as well as the variance of the data!

## How can we use unsupervised clustering models for classification tasks?

Unsupervised clustering is classification task itself. It grouping your given data into various groups/classes/categories with respect to similarities of data points. A popular classifier for such tasks may be Nearest Neighbour or K-NN.

Do recommender systems use clustering?

Using clustering can address several known issues in recommendation systems, including increasing the diversity, consistency, and reliability of recommendations; the data sparsity of user-preference matrices; and changes in user preferences over time.

### Can clustering be used for recommendation?

In order to enhance the accuracy of recommendation results, we use -means clustering method to cluster users before recommending. As we know that users in a cluster will have similar interests, thus, if a product is selected by these users, it will be suitable to the target user.

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How are K means and Gaussian mixture model related?

Gaussian mixture models can be used to cluster unlabeled data in much the same way as k-means. The second difference between k-means and Gaussian mixture models is that the former performs hard classification whereas the latter performs soft classification.