Difference Between Clustering and Classification

Key Difference – Clustering vs Classification

Though clustering and classification appear to be similar processes, there is a difference between them based on their meaning. In the data mining world, clustering and classification are two types of learning methods. Both these methods characterize objects into groups by one or more features.The key difference between clustering and classification is that clustering is an unsupervised learning technique used to group similar instances on the basis of features whereas classification is a supervised learning technique used to assign predefined tags to instances on the basis of features.

What is Clustering?

Clustering is a method of grouping objects in such a way that objects with similar features come together, and objects with dissimilar features go apart. It is a common technique for statistical data analysis used in machine learning and data mining. Clustering can be used for exploratory data analysis and generalization.

Clustering belongs to unsupervised data mining, and clustering is not a single specific algorithm, but a general method to solve the task. Clustering can be achieved by various algorithms. The appropriate cluster algorithm and parameter settings depend on the individual data sets. It is not an automatic task, but it is an iterative process of discovery. Therefore, it is necessary to modify data processing and parameter modeling until the result achieves the desired properties. K-means clustering and Hierarchical clustering are two common clustering algorithms used in data mining.Difference Between Clustering and Classification

What is Classification?

Classification is a process of categorization where objects are recognized, differentiated and understood on the basis of the training set of data. Classification is a supervised learning technique where a training set and correctly defined observations are available.

The algorithm which implements classification is often known as the classifier, and the observations are often known as the instances. K-Nearest Neighbor algorithm and decision tree algorithms are the most famous classification algorithms used in data mining.Key Difference - Clustering vs Classification

What is the difference between Clustering and Classification?

Definitions of Clustering and Classification:

Clustering: Clustering is an unsupervised learning technique used to group similar instances on the basis of features.

Classification: Classification is a supervised learning technique used to assign predefined tags to instances on the basis of features.

Characteristics of  Clustering and Classification:


 Clustering: Clustering is an unsupervised learning technique.

Classification: Classification is a supervised learning technique.

Training Set:

Clustering: A training set is not used in clustering.

Classification: A training set is used to find similarities in classification.


Clustering: Statistical concepts are used, and datasets are split into subsets with similar features.

Classification: Classification uses the algorithms to categorize the new data according to the observations of the training set.


Clustering: There are no labels in clustering.

Classification: There are labels for some points.


Clustering: The aim of clustering is, grouping a set of objects in order to find whether there is any relationship between them.

Classification: The aim of clustering is to find which class a new object belongs to from the set of predefined classes.

Clustering vs.Classification – Summary

Clustering and classification can seem similar because both data mining algorithms divide the data set into subsets, but they are two different learning techniques, used in data mining for the purpose of getting reliable information from a collection of raw data.

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“Cluster-2″ by Cluster-2.gif: hellisp derivative work: (Public Domain) via Wikimedia Commons 
“Magnetism” by John Aplessed – Own work. (Public Domain) via Commons