Concise Guide To Unsupervised Learning With Clustering!

Concise Guide To Unsupervised Learning With Clustering!

Detailed understanding of the concepts of unsupervised learning with the help of clustering algorithms. Clustering and association are two of the most important types of unsupervised learning algorithms. Today, we will be focusing only on Clustering.

Machine learning tasks usually have some data sets where we have some parameters, and for those resulting parameters, we have their respective outputs. From these datasets, our machine learning model built can predict the results for similar data. This process is what happens in supervised learning.

An example of supervised learning is for determining if the patient appears to have a tumor. We have a large dataset with a set of parameters of their patients matched with their respective results. We can assume that this is a simple classification task with ‘1’ for tumor and ‘0’ for None.

However, let’s say we have a dataset of dogs and cats. There are no pre-trained results for us to determine which one of them is a cat or a dog. Such kind of problems that have unlabeled datasets can be solved with the help of unsupervised learning. In technical terms, we can define unsupervised learning as a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Clustering and association are two of the most important types of unsupervised learning algorithms. Today, we will be focusing only on Clustering.

Clustering:

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Source: Wikipedia

Using certain data patterns, the machine learning algorithm is able to find similarities and group these data into groups. In other words, Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).

In clustering, we don’t have any predictions or labeled data. We are given a set of input data points, and using these we need to find the most similar matches and group them into clusters. The clustering algorithms have a wide range of applications that we will discuss in future sections.

Let us analyze the various clustering algorithms that are available. We will discuss the three most prevalent and popular algorithm techniques among the many existing approaches available to us. We will also understand the performance metrics used for unsupervised learning and finally discuss their applications in the real world.

data-science machine-learning unsupervised-learning clustering

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