How do you overcome the drawbacks of K means clustering?

How do you overcome the drawbacks of K means clustering?

One way to overcome those weaknesses is to use K-mean clustering only if there are available many data. To overcome outliers problem, we can use median instead of mean. Some people pointed out that K means clustering cannot be used for other type of data rather than quantitative data.

Is the limitation of K-means clustering?

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.

What are two challenges of k-means clustering?

We need to declare the number of clusters before running k-means clustering algorithm. It is not capable of determining the optimum number of clusters. K-means partitions the data set into the number of clusters determined by us beforehand. Finding the optimum number of clusters is also a challenging task for us.

Is it possible to find k in k-means?

K-Means is the most used clustering algorithm in unsupervised Machine Learning problems and it is really useful to find similar data points and to determine the structure of the data. In this article, I assume that you have a basic understanding of K-Means and will focus more on how you can-

Why does k-means not take account of data densities?

Therefore, it does not take into account the different densities of each cluster. So, because K -means implicitly assumes each cluster occupies the same volume in data space, each cluster must contain the same number of data points.

How to find the value of K in clusters?

Find the value of K (number of clusters) using different methods like the Elbow curve, Sillhouette curve, and Intercluster distances. A great visualization library YellowBrick which can help you to plot these curves with just 1 line of code.