Clustering vs Classification: Difference and Comparison

In today’s world, machine learning is very important as artificial intelligence is seen as an integral part of it. The study of computer algorithms by using data is what machine learning does.

They collect data, also known as ‘training data, to predict how they will perform the tasks. Machine learning is used in a variety of areas, such as in medicine, filtering of emails, etc.

Clustering and Classification use statistical methods for collecting data, especially in the field of machine learning.

Key Takeaways

  1. Clustering is a technique used to group similar data points based on their characteristics, while classification categorises data into pre-defined classes based on their features.
  2. Clustering is more useful when there is no prior knowledge of the data, and the aim is to discover underlying patterns. At the same time, classification is more suitable when the goal is to assign new data to pre-existing categories.
  3. Various clustering algorithms include k-means, hierarchical, and DBSCAN, while various classification algorithms include decision trees, logistic regression, and support vector machines.

Clustering vs Classification

Clustering groups data points based on similarities without pre-defined categories, while classification assigns data points to predetermined classes using supervised learning. The key difference lies in the learning approach: clustering employs unsupervised techniques, and classification relies on supervised methods.

Clustering vs Classification

Clustering is also called cluster analysis in machine learning. It is the process in which an object is grouped in such a way that the objects inside the clusters have similar properties, but when compared to another cluster, it is very much dissimilar to it.

This technique of clustering is used in statistical and explorative data analysis in processes such as image analysis, data compression, information retrieval, pattern recognition, bioinformatics, computer graphics, and machine learning.

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Classification is also called statistical classification in machine learning. It is a process in which the objects are classified and put into a set of categorized compartments.

Classification is done on quantifiable observations. An algorithm that incorporates the classification is known as a classifier. Classification is based on a two-step process: the learning and classification steps.

Comparison Table

Parameters of ComparisonClusteringClassification
DefinitionClustering is a technique in which objects in a group are clustered having similarities. Classification is a process in which observation is classified given as input by a computer program.
DataClustering does not require training data.Classification requires training data.
PhaseIt includes single-stage, i.e., grouping.It includes two-step: training data and testing.
LabellingIt deals with unlabelled data.It deals with both labeled and unlabelled data in its processes.
ObjectiveIts main objective is to unravel the hidden pattern as well as narrow relationships.Its objective is to define the group to which objects belong to.

What is Clustering?

Clustering is part of machine learning that groups the data into clusters with high similarity, but different clusters may differ. It is a method of unsupervised learning and is very commonly used for statistical data analysis.

There are different types of clustering algorithms like K-means, DBSCAN, Fuzzy C-means, Hierarchical clustering, and Gaussian (EM).

Clustering does not require training data. Compared to classification, clustering is less complex as it includes only data grouping. It does not give labels to every group like classification.

It has a single-step process known as Grouping. Clustering can be formulated as a multi-objective optimization problem focusing on multiple problems.

Clustering was first created by Driver and Kroeber in the field of anthropology in the year 1932. Then it was introduced to the various field by various persons.

Cartell used popular clustering for trait theory classification in personality psychology in 1943. It can be roughly distinguished as Hard Clustering and Soft Clustering.

It has different applications, such as customer segregation, social network analysis, detecting dynamic data trends, and cloud computing environments.


What is Classification?

Classification is basically used for pattern recognition, where the output value is given to the input value, just like clustering. Classification is a technique used in data mining but also used in machine learning.

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In Machine Learning, output plays an important role, and there comes the need for Classification and Regression. Both are supervised learning algorithms, unlike clustering.

When output has a discreet value, then it is considered as a classification problem. Classification algorithms help predict the output of a given data when input is provided to them.

There can be various types of classifications like binary classification, multi-class classification, etc.

Different types of classification also include Neural Networks, Linear Classifiers: Logistic Regression, Naïve Bayes Classifier: Random Forest, Decision Trees, Nearest Neighbor, and Boosted Trees.

Various Applications Of the Classification Algorithm includes Speech recognition, Biometric identification, Handwriting recognition, Email Spam Detection, Bank Loan Approval, Document classification, etc. Classification requires training data, and it requires predefined data, unlike clustering. It is a very complex process. It is a result of supervised learning. It deals with both labeled and unlabeled data. It involves two processes: training and testing.

Main Differences Between Clustering and Classification

  1. Clustering is a technique in which group objects are clustered with similarities. It is a result of supervised learning. Classification is a process in which observation is classified given as input by a computer program. It is a result of unsupervised learning.
  2. Clustering does not require training data. Classification requires training data.
  3. Clustering includes single-stage, i.e., grouping. The classification includes two-step: training and testing.
  4. Clustering deals with unlabelled data. Classification deals with both labeled and unlabelled data in its processes.
  5. Clustering main objective is to unravel the hidden pattern as well as narrow relationships. The classification objective is to define the group to which objects belong.
Difference Between Clustering and Classification

Last Updated : 18 June, 2023

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24 thoughts on “Clustering vs Classification: Difference and Comparison”

  1. This information is very useful to understand the key differences between clustering and classification, as well as their applications.

  2. The comparison table is particularly helpful to understand the parameters of comparison between clustering and classification. It’s clear and concise.

    • I appreciate that the data requirements for clustering and classification are highlighted. It’s an essential factor to consider in real-world applications.

  3. The detailed explanation of classification, including the different types of classifiers, provides a comprehensive understanding of this machine learning technique.

    • Indeed, the article provides valuable insights into the varied applications of classification algorithms and their significance in the field of machine learning.

  4. The clear explanations of clustering and classification are highly informative and provide a comprehensive overview of these machine learning techniques.

  5. The distinction between Hard Clustering and Soft Clustering is an intriguing aspect of the article and adds depth to the discussion of clustering.

  6. The detailed descriptions of clustering and classification, along with their respective algorithms, offer a well-rounded understanding of these machine learning methods and their relevance in various applications.

    • Definitely. The article effectively conveys the significance of clustering and classification in addressing real-world data analysis challenges across different domains.

    • Definitely. Understanding the origins of these concepts helps contextualize their significance in modern data analysis and machine learning.

  7. The emphasis on supervised learning approaches and the significance of the output value in classification is well-articulated and enriches the understanding of these concepts.

    • Absolutely. It’s a crucial aspect to consider when delving into the practical implementation of classification algorithms.

  8. The applications mentioned for both clustering and classification are diverse and demonstrate the relevance of these techniques across various domains.

    • I completely agree. It’s impressive to see how these methods can be applied in practical scenarios, from customer segregation to cloud computing.


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