Clustering is used for things like feature engineering or pattern discovery. A cluster is a group of data points that are similar to each other based on their relation to surrounding data points. Using a clustering algorithm means you're going to give the algorithm a lot of input data with no labels and let it find any groupings in the data it can. You might also hear this referred to as cluster analysis because of the way this method works. What are clustering algorithms?Ĭlustering is an unsupervised machine learning task. It's one of the methods you can use in an unsupervised learning problem. That's where clustering algorithms come in. You don’t know if there are any patterns hidden in the data, so you leave it to the algorithm to find anything it can. Unsupervised learning means you have a data set that is completely unlabeled. You can work around this by using a combination of supervised and unsupervised learning. This covers a large amount of real world data because it can be expensive to get an expert to label every data point.

With semi-supervised learning, you have a large data set where some of the data is labeled but most of it isn't. That's like knowing car prices based on features like make, model, style, drivetrain, and other attributes. In supervised learning you have labeled data, so you have outputs that you know for sure are the correct values for your inputs. You can go with supervised learning, semi-supervised learning, or unsupervised learning. There are three different approaches to machine learning, depending on the data you have.

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