What is Clustering?
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Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.
Some common clustering algorithms are:
K-Means Clustering K-Means is probably the most well-known clustering algorithm. It’s taught in a lot of introductory data science and machine learning classes. It’s easy to understand and implement in code.
Mean Shift Clustering Mean shift clustering is a sliding-window-based algorithm that attempts to find dense areas of data points. It is a centroid-based algorithm meaning that the goal is to locate the center points of each group/class, which works by updating candidates for center points to be the mean of the points within the sliding-window.
Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) Gaussian Mixture Models (GMMs) give us more flexibility than K-Means. With GMMs we assume that the data points are Gaussian distributed; this is a less restrictive assumption than saying they are circular by using the mean. That way, we have two parameters to describe the shape of the clusters: the mean and the standard deviation! Taking an example in two dimensions, this means that the clusters can take any kind of elliptical shape (since we have a standard deviation in both the x and y directions). Thus, each Gaussian distribution is assigned to a single cluster.
I recommend that you search about these algorithms and study them in detail. It will give you a really good idea how machine learning algorithms really work. There are more clustering algorithms as well. So feel free ^_^ Now let the Lab begin.. :D