Classification Vs. Clustering - A Practical Explanation

Classification and clustering are two methods of pattern identification used in machine learning. Although both techniques have certain similarities, the difference lies in the fact that classification uses predefined classes in which objects are assigned, while clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other groups of objects. These groups are known as "clusters".


In the field of machine learning, clustering is framed in unsupervised learning; that is, for this type of algorithm we only have one set of input data (not labelled), about which we must obtain information, without previously knowing what the output will be.

Clustering is used in projects for companies that want to find common aspects within their customers to apply customer segmentation, create customer journey maps or find groups and focus products or services. Thus, if a significant percentage of customers have certain aspects in common (age, type of family, etc.) the company can justify a particular campaign, service or product. Clustering is also useful to obtain general insights and information.

On the other hand, classification belongs to supervised learning, which means that we know the input data (labeled in this case) and we know the possible output of the algorithm. There is the binary classification that responds to problems with categorical answers (such as "yes" and "no", for example), and the multiclassification, for problems where we find more than two classes, responding to more open answers such as "great", "regular" and "insufficient".

Classification is used in many fields, such as biology or in the Dewey decimal classification for books, in the detection of spam in e-mails...

At Bismart we use classification and clustering in our projects, which are framed in many different sectors. For example, in the social services industry, we have used clustering to identify population groups that use specific social services. From social services data, we have been able to identify or cluster groups of people who use similar services according to their attributes (number of people in their charge, degree of dependency, marital status...). Thus, we have been able to detect what type of service a new user of social services will need beforehand by comparing their attributes with those of the clusters.

Classification is used when you need to know users or customers to decide which products or campaigns will be launched in the future. For example, at Bismart we developed a project for the insurance industry in which the client needed to classify customers according to accident claims, so that the policy could be classified according to the number of claims predicted. Thus, the company can choose the costumers with the lowest number of claims.



A well-known application of clustering algorithms are Netflix recommendation systems. Although the company is quite discreet with its algorithms, it is confirmed that there are about 2,000 clusters or communities that have common audiovisual tastes. Cluster 290 is the one that includes people who like the series "Lost", "Black Mirror" and "Groundhog Day". Netflix uses these clusters to refine its knowledge of the tastes of viewers and thus make better decisions in the creation of new original series.

Fraud Detection

Classification is commonly used in the financial sector. In the era of online transactions where the use of cash has decreased markedly, it is necessary to determine whether movements made through cards are safe. Entities can classify transactions as correct or fraudulent using historical data on customer behavior to detect fraud very accurately.



What’s the difference between supervised and unsupervised Machine Learning?

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