Understand a Few Clustering Algorithms That Are Good for Data Scientists

Algorithms are a foundational pillar of data science. However, data scientists understand the plethora of algorithms needed in the industry. Algorithms are responsible for making sense of the oceans of data available to data scientists. However, it is critical to understand how these algorithms work to implement them in your projects better. Clustering algorithms are only one type of the many such algorithms available. Machine learning clustering is also crucial because it allows you to build machine learning models that learn quickly without using too much computational power.

Clustering is when there are data points that group together. The goal of clustering algorithms is to take those data points and group them based on similar features or traits. This allows you to make sense of data that seems random. It’s also great to identify what data means without too much computational power or technical acumen. That’s because most of these algorithms are really easy to understand and implement. When you start to get into them, you start seeing valuable insights without many downsides.

K-Means Clustering Algorithms

One major clustering example is the K means clustering algorithm. It’s one of the best examples because it is easy to implement and inexpensive to run. Data scientists use this clustering algorithm because it offers excellent performance for the resources required. K means clustering works by selecting a few data clusters that you then randomly choose a center point for. You then work through each data point in the cluster, solving for the distance between that point and the center.

You then run this process and iterate through the algorithm until you’ve reached a point where nothing changes. Because of the speed of these calculations, you can easily do these things without too much computational intensity. The downside of this algorithm is that you have to select the groups yourself. Obviously, it’s not going to be great if you are analyzing data to get more information from it in the first place.

Mean-Shift Clustering

K means clustering is not the only clustering algorithm. In fact, it’s one of the many available in the industry. The next clustering algorithm we can talk about is the mean-shift clustering algorithm. It works a little bit differently because it uses a sliding window approach. Essentially, you slide a window across a group of points and calculate whether you are in the center or not each time.

There is a post-processing stage that attempts to ensure that you are actually at the right place before this clustering algorithm terminates. It works well because it also detects the number of clusters for you, making it possible to get even more information without having to dive even deeper into the data.

Other Clustering Algorithms

These two clustering algorithms are only the tip of the iceberg. In fact, you have access to many more clustering algorithms that are useful for developing machine learning clustering models and other important results when it comes to data science work. For example, you also have clustering algorithms like:

• DBSCAN
• Gaussian Mixture Models
• Agglomerative Hierarchical Clustering

These are only a few of the many options available. It’s important to understand that clustering algorithms will only continue to be important for data scientists and people in the machine learning and AI industry.