Understand Clustering Algorithms


Understand Clustering Algorithms

xpresso.ai Team
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Almost every machine learning task will involve clustering algorithms. Data clustering is one of the easiest ways to get generalized information without too much effort. Clustering is one of the first go-to methods that machine learning engineers use when finding answers from a data source. The main reason that these types of algorithms are so popular is that you almost have to do it when you go through the data preparation step.

The overwhelming majority of datasets you get will start off being largely unclassified and unstructured. You will need to get at least some basic information from this dataset before you start working on something bigger and deeper. Many people don’t even need to go further than this. Machine learning clustering is enough to get the results they are looking for. However, if clustering techniques are insufficient, you might have to go deeper. At those times, you need to understand how data clustering works and the various clustering algorithms available for you to use.

When Do Clustering Algorithms Make Sense?

The main thing to know is that clustering algorithms are great for many tasks but fail when looking for deep insights. The way you do clustering also can cause problems when performing data analysis. The more complex your machine learning problem, the less you want to use clustering techniques. Machine learning clustering is a great way for you to get surface-level information without having to do a lot of deep analytical work.

You also get more bang for your buck based on the clustering algorithms you choose. It happens like this because clustering techniques are there for you for classification work rather than to dig deep into data and get insights from it. However, the great thing about clustering methods is that they are great for moving forward with the data discovery step.

Use Cases for Clustering Algorithms

The first reason to use data clustering is when looking at a large unstructured data set. Clustering is great here because it allows you to get deep insights without going through a lot of work. If you have hundreds of gigabytes of data that you don’t want to do deep analysis on, clustering techniques are a great way of getting at least a bit of information from that data set without having to do deep analysis.

The other area where clustering techniques are valuable is when you don’t want to spend a lot of effort annotating data. If you would need to go through and manually annotate and divide your data set, it might make sense to do basic machine learning clustering on that data set. Clustering methods here would be a great way for you to at least have valuable insights without needing more detailed work.

Clustering is also a great way to find issues with your data. Clustering algorithms are great for finding anomalies, which would be useful because you might want to do a better analysis later, and those algorithms you would choose are not good for working with data with many anomalies.

Comparison of Clustering and Classification

There are a few differences between clustering and classification that you need to be aware of. They are both methods of classification when working with machine learning, but there are areas where they deviate. Clustering methods are used to group objects based on similarities they might have.

Classification is about identifying an object by labeling them to prepare for supervised machine learning. Once you understand how clustering algorithms work, you will understand where you can use them and how to make your machine learning clustering work better.

What are Some Different Clustering Methodologies?

Centroid-based Clustering – K-means is an example of this type of clustering. It organizes data into non-hierarchical clusters. They are sensitive to initial conditions but benefit from being more efficient.

Density-based Clustering – Allows for arbitrarily shaped clusters because it focuses on areas of high density. Because of that, these algorithms have problems when the data has varying densities.

Distribution-based Clustering – Assumes data is based on distributions.

Hierarchical Clustering – Best suited for hierarchical data. Taxonomies are an example of this.