{"id":80082,"date":"2020-10-23T09:00:10","date_gmt":"2020-10-23T03:30:10","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=80082"},"modified":"2020-10-23T09:00:10","modified_gmt":"2020-10-23T03:30:10","slug":"clustering-in-machine-learning","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/clustering-in-machine-learning\/","title":{"rendered":"Clustering in Machine Learning"},"content":{"rendered":"

In this article, we will study about an unsupervised learning-based technique known as clustering in machine learning.<\/strong>Here, we will discuss what clustering really is, What are its types? And we will look at the algorithms involved in the clustering technique.<\/p>\n

Clustering in Machine Learning<\/h3>\n

Let\u2019s try to understand what clustering exactly is. Examples make the job a lot more easier.<\/p>\n

So, as we know, there are two types of learning: active<\/strong> and passive<\/strong>.<\/p>\n

Passive means that the model follows a certain pre-written path and is also done under supervision. This doesn\u2019t mean something like we will take unsupervised learning on the active side. It is just that the human intervention in unsupervised learning is quite minimal as compared to supervised learning.<\/p>\n

Also, we have unlabeled data in unsupervised learning. So, here, the algorithm has to completely analyze the data, find patterns, and cluster the data depicting similar features.<\/p>\n

For example, if we provide a dataset consisting of images of two different objects. The model will scan the images for certain features. If some images have matching features, it will form a cluster.<\/p>\n

Note:-<\/strong><\/em>Active learning is a different concept. It\u2019s applicable for semi-supervised and reinforcement learning techniques.<\/p>\n

Examples of Clustering in Machine Learning<\/h4>\n

A real-life example would be: -Trying to solve a hard problem in chess. The possibilities to checkmate the king are endless. There is no predefined or pre-set solution in chess. You have to analyze the positions, your pieces, the opponent\u2019s pieces and find a solution.<\/p>\n

These traits to find the solution are the dataset, you are the model that has to analyze and find the answer. This is what unsupervised<\/strong> learning<\/strong> is.<\/p>\n

Now let\u2019s understand clustering. In clustering, we classify data points into clusters based on similar features rather than labels. The labelling part in clustering comes at the end when clustering is over.<\/p>\n

Also, we should add a lot of data to the dataset, to increase the accuracy of the results. The algorithm will learn various patterns in the dataset.<\/p>\n

Like, it will look for certain traits and features and compare the similarities<\/strong> between the data.<\/p>\n

There is a difference here between classification and clustering. The labeling part involves a lot of human intervention that proves both costly and time-consuming. Although labelling gets fairly simple in unsupervised learning, the model would have to process more due to more analysis.<\/p>\n

Let\u2019s take an example. If we have a dataset consisting of images of tigers, zebras, leopards, cheetahs.<\/p>\n

The clustering algorithm would analyze this dataset and then divide the data based on some specific characteristics. The characteristics would include fur color, patterns (spots, stripes), face shape, etc.<\/p>\n

The model would remember the pattern in which it classified the data. This knowledge will come in handy for future unknown data.<\/p>\n

We also have other applications of clustering like fake-news detection, fraud detection, spam mail segregation, etc. Now, let\u2019s dive a bit deeper into clustering now that we have seen and understood it.<\/p>\n

There are various types of clustering that we should know about. These will help us to further classify and understand the various algorithms that unsupervised learning has.<\/p>\n

These include:<\/p>\n