Do we need to normalize data for K-means clustering?

Normalization is not always required, but it rarely hurts. Some examples: K-means: K-means clustering is “isotropic” in all directions of space and therefore tends to produce more or less round (rather than elongated) clusters.

How do you determine the value of K in K-means clustering?

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.

Which of the following method is used for finding optimal of cluster in K mean algorithm?

Which of the following method is used for finding optimal of cluster in K-Mean algorithm? Out of the given options, only elbow method is used for finding the optimal number of clusters.

How does K-means clustering work?

K-means clustering uses “centroids”, K different randomly-initiated points in the data, and assigns every data point to the nearest centroid. After every point has been assigned, the centroid is moved to the average of all of the points assigned to it.

Why do you need to scale the data before applying K-means algorithm?

This will impact the performance of all distance based model as it will give higher weightage to variables which have higher magnitude (income in this case). Hence, it is always advisable to bring all the features to the same scale for applying distance based algorithms like KNN or K-Means.

Should I scale data for K-means?

It depends on your data. If you have attributes with a well-defined meaning. Say, latitude and longitude, then you should not scale your data, because this will cause distortion. ( K-means might be a bad choice, too – you need something that can handle lat/lon naturally)

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.

How do K Medoids work?

k -medoids is a classical partitioning technique of clustering that splits the data set of n objects into k clusters, where the number k of clusters assumed known a priori (which implies that the programmer must specify k before the execution of a k -medoids algorithm).

Do you need to scale for K-means?

Yes, in general, attribute scaling is important to be applied with K-means. Most of the time, the standard Euclidean distance is used (as a distance function of K-means) with the assumption that the attributes are normalized.