Clustering algorithms

What is Clustering?

Clustering is an unsupervised ML activity that involves grouping data. These groupings are formed by revealing hidden patterns in the data and then grouping data points with similar patterns together in the same cluster. Clustering’s key benefit is its capacity to make sense of unlabeled data.

Unlabeled data is a sort of information that is plentiful and very simple to collect. It may be a collection of photographs scraped from the web, a corpus of tweets, or any other unlabeled collection of data points. Labeled data, on the other hand, has a label attached to it. This might be a dataset of tagged pictures or a corpus of words with each text labeled.

  • Labeled data is more useful, but it’s hard to come by since labeling is a time-consuming procedure that typically necessitates human annotators manually assigning labels to each data point.

To learn, all machine learning algorithms require data, but the type of data — whether labeled or unlabeled — determines which algorithms we can use on it. We may distinguish between two types of machine learning techniques based on this: supervised and unsupervised learning.

As the name implies, supervised machine learning learns from data labels that give supervision. Data points are mapped to labels as a consequence of learning for these algorithms. Unsupervised learning, on the other hand, lacks the signal given by the data’s labels. Unsupervised learning algorithms, on the other hand, use a variety of statistical approaches to generate labels.

Classification of Clustering algorithms

Despite the fact that clustering algorithms have a large knowledge base, there is no agreement on how they should be classified. They will be classified differently by different sources based on different criteria. In our opinion, there are two types of categorizations that are effective in practice:

  • based on the number of clusters to which a data point may belong
  • based on the forms of the resultant clusters.

The first difference is between “hard” and “soft” clustering techniques. A data point may be a member of only one cluster- hard clustering of numerous clusters with various degrees of membership- soft clustering. This is a crucial issue since, depending on your application, you may want your clusters to be rigid or overlap.

The other classification is based on the form and kinds of clusters produced by an algorithm. There are numerous various cluster types in this category, with hierarchical, centroid-based, and density-based methods being some of the more well-known.

It’s worth noting that these two classifications aren’t mutually exclusive. K-means clustering is both hard and centroid-based. There are numerous various cluster types in this category, with hierarchical, centroid-based, and density-based methods being some of the more well-known. In the following part, we’ll go through these in further depth.

It’s worth noting that these two classifications aren’t mutually exclusive. For example, k-means clustering, a common clustering technique, is both hard and centroid-based. Nevertheless, because many clustering methods may not strictly belong to one clustering technique, these classifications should be treated with a grain of salt. As a result, the classifications are more of a suggestion for selecting the best algorithm for your application.

  • Clustering of Centroids– The fundamental goal of centroid clustering is to locate the dataset’s centroids. The clusters’ centers are centroids, whereas the clusters themselves are formed by allocating each data point to the centroid nearest to it. Unlike other clustering approaches, which automatically determine the ideal number of clusters, centroid-based algorithms need the analyst to choose this number ahead of time. As a result, it’s critical to have a sense of the clusters that may exist in your data before using the technique.
  • Clustering based on density– Density-based clustering analyzes only locations with a high concentration of data points, whereas the previous two techniques utilize all data points when building a cluster. Outliers are data points that fall outside of a manually defined radius and are removed from the study.
  • Clustering by Hierarchy– A cluster rating system is constructed using hierarchical clustering. This method of clustering discovers the primary, distinct clusters as well as subclusters, or clusters that occur inside larger clusters. If you wish to find hidden substructures in your data, hierarchical clustering is more beneficial than centroid-based approaches.


Clustering, like so much of data science, is based on trial and error.

A clustering algorithm’s result does not always make sense right away. Before determining whether to attempt another technique, the analyst must assess the importance of the generated clusters.

There are many algorithms to pick from because clustering is one of the oldest and most investigated ML approaches in principle. It’s a good idea to study the benefits of the various algorithms, such as the sort of data and jobs they’re best suited for and the type of clusters they generate, to reduce time spent on the trial-and-error process.

The initialization parameters of several clustering methods are critical. You may just need to run the algorithm a few more times in such circumstances. In other circumstances, the chosen approach simply cannot cluster your data meaningfully, forcing you to switch to another.

Another thing to think about is the magnitude of your data. Hierarchical clustering techniques, for example, have a cubic time complexity, which means they struggle with huge datasets. If a transition from a hierarchical to a centroid-based approach is appropriate in such cases, k-means would be a preferable alternative, since its runtime is a magnitude of order less. As a result, the size of the dataset might have a significant impact on the algorithm’s performance.

Lastly, think about the data you’re working with. Many clustering algorithms calculate clusters using a distance-based metric, which implies your dataset’s characteristics must be numeric. Although categorical variables may be converted to binary values in a single step, calculating distances between them makes little sense. You might also use k-modes clustering, which is designed to handle both numeric and categorical data or a different strategy entirely.

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