Can we use K-Means clustering for supervised learning?

Can we use K-Means clustering for supervised learning?

The k-means clustering algorithm is one of the most widely used, effective, and best understood clustering methods. In this paper we propose a supervised learning approach to finding a similarity measure so that k-means provides the desired clusterings for the task at hand.

Is K-Means clustering supervised unsupervised learning explain the answer?

K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. There is a way of finding out what is the best or optimum value of K for a given data.

In which condition k-means does not work properly?

k-means assume the variance of the distribution of each attribute (variable) is spherical; all variables have the same variance; the prior probability for all k clusters are the same, i.e. each cluster has roughly equal number of observations; If any one of these 3 assumptions is violated, then k-means will fail.

Can clustering be supervised?

We study a recently proposed framework for supervised clustering where there is access to a teacher. We give an improved generic algorithm to cluster any concept class in that model. Our algorithm is query-efficient in the sense that it involves only a small amount of interaction with the teacher.

What is the purpose of K-means clustering?

Kmeans clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of kmeans is to group data points into distinct non-overlapping subgroups.

How is K-means clustering used?

K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The centroids of the K clusters, which can be used to label new data. Labels for the training data (each data point is assigned to a single cluster)

How many clusters k-means?

The Silhouette Method Average silhouette method computes the average silhouette of observations for different values of k. The optimal number of clusters k is the one that maximize the average silhouette over a range of possible values for k. This also suggests an optimal of 2 clusters.

How is k-means clustering used in machine learning?

Like many other unsupervised learning algorithms, K-means clustering can work wonders if used as a way to generate inputs for a supervised Machine Learning algorithm (for instance, a classifier). The inputs could be a one-hot encode of which cluster a given instance falls into, or the k distances to each cluster’s centroid.

How to adapt k-means to naturally imbalanced clusters?

To cluster naturally imbalanced clusters like the ones shown in Figure 1, you can adapt (generalize) k-means. In Figure 2, the lines show the cluster boundaries after generalizing k-means as:

What are the advantages and disadvantages of k-means?

Clustering data of varying sizes and density. k-means has trouble clustering data where clusters are of varying sizes and density. To cluster such data, you need to generalize k-means as described in the Advantages section.

When to use semi supervised clustering in classification?

Clustering in such a situation where some examples have labels attached to them is known as semi-supervised clustering. If on the other hand, were we to build a classifier using such a set of partially labeled examples, we would term the situation as semi-supervised classification.