Machine Learning Clustering: 5 Algorithms You Need To Know

Data scientists use different approaches to machine learning, depending on what data they have. One of the most common options is unsupervised learning. This method refers to unlabeled data sets. Since the data is unlabeled, you can predict any hidden patterns in the data. Thus, you leave it to the algorithm to find anything. This algorithm is known as the ‘clustering algorithm’.

Today, we’re sharing this helpful guide on what machine learning clustering is and the common algorithms available. 

What Is Clustering Algorithms?

Clustering refers to grouping objects based on their similarities and mainly determines the grouping with unlabeled data. There’s no set criterion for good clustering and it mainly depends on the specific scenario and users. 

Clustering is a tricky concept. This is why there are numerous clustering algorithms available. Different cluster models are used, and for each of these cluster models, a different clustering algorithm can be applied. 

In addition, clusters discovered by one clustering algorithm will be different from those found by another algorithm. 

Types Of Clustering Algorithms

1. K-Means Clustering

K-means is considered the most commonly used algorithm due to its simple and centroid-based algorithm. In fact, K-means is also how most people are introduced to unsupervised machine learning. 

K-means can reduce the variance of data points within a single cluster. It’s most suitable for smaller data sets since it tends to repeat all data points in a circular format. 

Thus, if used in larger data sets, it can be quite time-consuming to classify data points. Because of its time-consuming way of clustering data points, k-means also doesn’t scale well. 

2. Gaussian Mixture Model

The Gaussian mixture model is a better alternative to k-means, especially if working with a non-circular data set. Gaussian mixture models don’t need circular-shaped data for them to work well. Instead, it uses multiple Gaussian distributions to fit randomly shaped data. 

As mentioned before, k-means follows a circular format in clustering data. As a result, non-circular data isn’t clustered correctly.

In Gaussian mixture model, there are several single Gaussian models acting as hidden layers. The algorithm calculates the probability of a data point belonging to a specific Gaussian distribution, and that’s the cluster where it will fall under. 

3. BIRCH

If the k-means algorithm is suitable for smaller data sets, then BIRCH is the exact opposite. Short for Balance Iterative Reducing and Clustering using Hierarchies, the BIRCH algorithm can process large data sets more easily. 

This algorithm breaks the data into small summaries and then clusters them. These summaries can hold as much maximum distribution information about data points as possible. 

Data scientists often use BIRCH along with other clustering algorithms. This is because the summaries created by BIRCH can be optimized more by other clustering algorithms.

Unfortunately, BIRCH only works on numerical data values. You can use categorical values if you do some data transformation. 

4. Mean-Shift Clustering

Another commonly used algorithm is mean-shift, which is particularly useful for handling computer vision processing and images. It uses a repetitive process with each data point, moving them closer to where other points are until all of them are assigned to a cluster. This is why mean-shift is often called the mode-seeking algorithm.

Like BIRCH, mean-shift is a hierarchical clustering algorithm that finds clusters without setting up an initial number of clusters. It also works like k-means in that it repeats over all data points and moves them towards the mode, which is the high-density area of data in a region. 

Unfortunately, it has the same downsides as k-means because it doesn’t scale well when processing large data sets. 

5. DBSCAN

Short for density-based spatial clustering of applications with noise, DBSCAN is a clustering algorithm suitable for finding outliers in a data set. 

This algorithm finds irregularly shaped clusters based on the density of the data points in various regions. Regions are separated by areas of low-density so that the algorithm can detect outliers between the high-density clusters. 

In general, DBSCAN uses two different parameters in defining clusters. And choosing the right parameters is critical for it to work effectively.

• Eps: The distance used to identify if a data point is in the same area as others.

• MinPts: The minimum number of data points that should be clustered together in order to be considered as a high-density area.

Take Away

And there you have it! 

As you can see, there are several forms of clustering algorithms used by data scientists. However, there’s no single best algorithm for all cases. 

Thus, it’s best to explore different clustering algorithms and understand the different structures for each algorithm to find the best option for your machine learning project.