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Clustering vs Classification: Difference Between Clustering & Classification

Clustering vs Classification: Difference Between Clustering & Classification

Q: 1. What is the difference between classification & prediction and cluster analysis?

Classification and prediction are both supervised learning tasks. In classification, the goal is to assign labels to data points based on labeled training data. Prediction, a subset of classification, involves predicting continuous output variables. On the other hand, cluster analysis is an unsupervised learning method where the objective is to group data points into clusters based on their similarities without prior labels.

Q: 2. How do regression, classification, and clustering compare in machine learning?

Regression, classification, and clustering are different types of machine learning techniques:Regression is used for predicting continuous values. For example, predicting house prices based on features like location and size.Classification involves assigning data points to predefined categories or classes. For instance, classifying emails as spam or not spam.Clustering is an unsupervised technique used to group similar data points together based on certain characteristics without predefined labels.

Q: 3. What is the role of classification and clustering in data mining?

In data mining, classification, and clustering play key roles in extracting meaningful patterns from data:Classification helps in identifying which category or class an instance belongs to, based on input data.Clustering helps in grouping similar data points, identifying hidden patterns in data, and discovering relationships that are not immediately apparent.

Q: 4. Is clustering supervised or unsupervised?

Clustering is an unsupervised learning technique. It groups similar data points together based on patterns and similarities without predefined labels. Unlike supervised learning, clustering does not use labeled data to train the model.

Q: 5. What is the difference between grouping and classifying?

Grouping (Clustering) is an unsupervised process where data points are categorized based on similarities without predefined labels. Example: Segmenting customers based on purchasing behavior.Classifying is a supervised process where data is assigned to predefined categories based on prior training. Example: Identifying whether an email is spam or not using labeled data.

Q: 6. What is an example of clustering?

A common example of clustering is customer segmentation in marketing. Companies use clustering algorithms to group customers based on purchasing patterns, demographics, or browsing behavior to offer personalized recommendations.

Q: 7. Which algorithm is commonly used for unsupervised learning?

One of the most widely used algorithms for unsupervised learning is K-Means Clustering. It partitions data into clusters based on similarity by minimizing the variance within each cluster. Other popular unsupervised algorithms include DBSCAN (Density-Based Spatial Clustering), Hierarchical Clustering, and Gaussian Mixture Models (GMM).

Q: 8. What are the different methods and applications of clustering?

Methods of Clustering:K-Means Clustering – Divides data into K clusters based on similarity.Hierarchical Clustering – Builds a hierarchy of clusters using a tree-like structure.DBSCAN (Density-Based Spatial Clustering) – Groups points based on density regions, useful for noisy data.Gaussian Mixture Models (GMM) – Uses probabilistic distributions to model clusters.Fuzzy C-Means – Allows data points to belong to multiple clusters with varying degrees of membership.Applications of Clustering:Customer Segmentation – Grouping customers based on purchasing behavior.Image Segmentation – Identifying different regions in images.Anomaly Detection – Fraud detection in banking and cybersecurity.Document Clustering – Organizing similar articles or research papers.Genomics & Bioinformatics – Identifying patterns in genetic data.

Q: 9. What are the different classifiers and applications of classification?

Types of Classifiers:Decision Trees – Uses tree-like structures to make decisions.Random Forest – Combines multiple decision trees for better accuracy.Support Vector Machines (SVM) – Finds an optimal boundary between classes.Naïve Bayes – Probabilistic classifier based on Bayes' theorem.Logistic Regression – Used for binary classification problems.K-Nearest Neighbors (KNN) – Classifies based on the majority class among neighbors.Applications of Classification:Email Spam Detection – Identifying spam vs. non-spam emails.Medical Diagnosis – Predicting diseases based on symptoms and test results.Credit Risk Analysis – Determining if a loan applicant is high-risk or low-risk.Speech Recognition – Converting spoken words into text.Sentiment Analysis – Classifying customer reviews as positive, negative, or neutral.

Q: 10. What are the most common classification algorithms in Machine Learning?

Logistic Regression – Used for binary classification problems.Decision Trees – Simple and interpretable tree-based classification.Random Forest – A powerful ensemble method combining multiple decision trees.Support Vector Machine (SVM) – Finds the optimal hyperplane for separating classes.Naïve Bayes – Based on conditional probabilities, often used for text classification.K-Nearest Neighbors (KNN) – Classifies data based on nearest neighbors.Neural Networks – Deep learning-based classification for complex tasks like image recognition.

By Rohit Sharma

Updated on Mar 07, 2025 | 21 min read | 48.5k views

Table of Contents

Machine Learning algorithms are generally categorized based upon the type of output variable and the type of problem that needs to be addressed. These algorithms are broadly divided into three types i.e. Regression, Clustering, and Classification. Regression and Classification are types of supervised learning algorithms while Clustering is a type of unsupervised algorithm.

When the output variable is continuous, then it is a regression problem whereas when it contains discrete values, it is a classification problem. Clustering algorithms are generally used when we need to create the clusters based on the characteristics of the data points. This article aims to give you a quick introduction to clustering and classification, and I’ll also highlight some key differences between the two.

Classification and clustering are the two most important parts of machine learning algorithms. People often mistake them to be the same; however, even if they appear to be slightly similar processes, the difference between clustering vs classification is significant. This article will provide an in-depth understanding of clustering and classification, along with a clustering vs classification comparison and the major difference between classification and clustering.

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Classification

Classification is a type of supervised machine learning algorithm. For any given input, the classification algorithms help in the prediction of the class of the output variable. There can be multiple types of classifications like binary classification, multi-class classification, etc. It depends upon the number of classes in the output variable.

The classification techniques help make predictions about the target values’ category based on any input provided. Usually, the term “classification” is used to narrate the predictive modeling in which the sample annotation is definite. Moreover, you can use a classification algorithm to allocate every data point to a particular class. For instance, you can label a pineapple as a fruit or vegetable in a database or categorize products based on department, segment, category, or subcategory.

Before moving on to exploring the types of classification and clustering, you must thoroughly know the detail of each of them. The first stage in classification is the training step and the second one denotes where to classify the data. You must train the algorithm on an appropriately classified dataset. So, it guarantees that the points in your dataset are correctly classified after you run the corresponding algorithm. After the data is classified, you can test the algorithm’s accuracy by assessing sensitivity and precision to recognize the correct output.

Before exploring classification vs clustering, let’s first look at the types of classification algorithms.

Types of Classification Algorithms

Logistic Regression: – It is one of the linear models which can be used for classification. It uses the sigmoid function to calculate the probability of a certain event occurring. It is an ideal method for the classification of binary variables.

K-Nearest Neighbours (kNN): – It uses distance metrics like Euclidean distance, Manhattan distance, etc. to calculate the distance of one data point from every other data point. To classify the output, it takes a majority vote from k nearest neighbors of each data point.

The classification and clustering differ a lot based on this category. Whenever a customer searches for a product on your website, the classification algorithm will demonstrate identical items that might be pertinent to the original search term. Moreover, other products that might be frequently bought with the product are also advised to the shopper during this point.

Decision Trees: – It is a non-linear model that overcomes a few of the drawbacks of linear algorithms like Logistic regression. It builds the classification model in the form of a tree structure that includes nodes and leaves. This algorithm involves multiple if-else statements which help in breaking down the structure into smaller structures and eventually providing the final outcome. It can be used for regression as well as classification problems.

Understanding the types of clustering and classification algorithms is important before assessing their differences. This type of classification algorithm marks a prominent difference between these two approaches. Decision Trees method prepares a binary tree with input variables (also known as nodes) and output variables (also known as predictions).

Decision trees assist you to map the consumer decision-making procedure for a specific product category represented as a consumer decision tree. Also, this method helps select a product that meets your needs. You can execute it as a questionnaire/quiz wherein each choice a shopper makes lead them to a final product recommendation.

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Random Forest: – It is an ensemble learning method that involves multiple decision trees to predict the outcome of the target variable. Each decision tree provides its own outcome. In the case of the classification problem, it takes the majority vote of these multiple decision trees to classify the final outcome. In the case of the regression problem, it takes the average of the values predicted by the decision trees.

Naïve Bayes: – It is an algorithm that is based upon Bayes’ theorem. It assumes that any particular feature is independent of the inclusion of other features. i.e. They are not correlated to one another. It generally does not work well with complex data due to this assumption as in most of the data sets there exists some kind of relationship between the features.

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Support Vector Machine: – It represents the data points in multi-dimensional space. These data points are then segregated into classes with the help of hyperplanes. It plots an n-dimensional space for the n number of features in the dataset and then tries to create the hyperplanes such that it divides the data points with maximum margin.

Along with the key features, you also need to learn the applications of clustering and classification. Let’s first go through the applications of the classification algorithm.

Read: Common Examples of Data Mining.

Applications

The evaluation of classification vs clustering differences is incomplete without understanding their applications. Both classification and clustering in data mining show us unique benefits. However, you also need to explore other applications of each of these approaches.

So far it is known that data classification is a data mining process that helps categorize items by assigning them to target categories or classes. Therefore, in any circumstance where a huge amount of data needs to be categorized, in order to make any task easier, classification is applied. Software companies often utilize data classification to fix their bugs quickly. The reason is categorizing cases and bug reports make it easier for them to detect the software malfunction and fix it.
The process of classifying data is also massively helpful for organizations that lack resources, especially employee resources who can perform such labor and time-intensive tasks. Therefore, this triage process often comes to the rescue of many such companies where a huge amount of data needs to be handled.
Another area of implementation of data classification can be found in the finance sector. The predictive facility of this approach helps find the suitable target class. For instance, it helps categorizing a large number of bank account holders into low, medium, or high credit risk categories.
If you want to thoroughly assess the clustering vs classification differences, you should first look at their major applications. Commonly, a classification algorithm is used in the financial sector to assure data security. Especially in the era of online transactions that marks the decreased use of cash, it is vital to decide whether money transfers made via cards are safe or not. Furthermore, entities can categorize transactions as correct or fake using the historical data on customer behavior.
Other areas of application include-

Email Spam Detection.
Facial Recognition.
Identifying whether the customer will churn or not.
Bank Loan Approval.

One of the major differences between clustering vs classification is that a classification algorithm is used for consumer behavior classification. You can use the classification to categorize your customer base based on certain factors.

For instance, you can classify shoppers based on brand loyalty for a specific brand. This information helps you to target non-brand loyal customers with marketing to promote brand switching.

The classification algorithm is used to build a model that can use gene expression data for predicting the forecast of a cancer patient. Moreover, it is used to build a model that can employ some numeric data to allocate a sample to one of the many disease subtypes.

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Clustering

Clustering is a type of unsupervised machine learning algorithm. It is used to group data points having similar characteristics as clusters. Ideally, the data points in the same cluster should exhibit similar properties and the points in different clusters should be as dissimilar as possible.

Clustering is divided into two groups – hard clustering and soft clustering. In hard clustering, the data point is assigned to one of the clusters only whereas in soft clustering, it provides a probability likelihood of a data point to be in each of the clusters.

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The classification and clustering difference highlights that the clustering algorithm adopts a single-phase approach. It means you fed the input data to the system without determining the groupings or output. This method helps you to set the clustering parameters which must align with your business goals and strategy. For instance, you can cluster a dataset based on sales, brand, subcategory, etc.

The clustering algorithm helps you to find the patterns and similarities in your customer base as well as product categories. In retail, the clustering algorithm helps you to cluster your data and convert it into a logical format from which you can produce insights.

Types of Clustering Algorithms

K-Means Clustering: – It initializes a pre-defined number of k clusters and uses distance metrics to calculate the distance of each data point from the centroid of each cluster. It assigns the data points into one of the k clusters based on its distance.

Agglomerative Hierarchical Clustering (Bottom-Up Approach): – It considers each data point as a cluster and merges these data points on the basis of distance metric and the criterion which is used for linking these clusters.

Divisive Hierarchical Clustering (Top-Down Approach): – It initializes with all the data points as one cluster and splits these data points on the basis of distance metric and the criterion. Agglomerative and Divisive clustering can be represented as a dendrogram and the number of clusters to be selected by referring to the same.

DBSCAN (Density-based Spatial Clustering of Applications with Noise): – It is a density-based clustering method. Algorithms like K-Means work well on the clusters that are fairly separated and create clusters that are spherical in shape. DBSCAN is used when the data is in arbitrary shape and it is also less sensitive to the outliers. It groups the data points that have many neighbouring data points within a certain radius.

OPTICS (Ordering Points to Identify Clustering Structure): – It is another type of density-based clustering method and it is similar in process to DBSCAN except that it considers a few more parameters. But it is more computationally complex than DBSCAN. Also, it does not separate the data points into clusters, but it creates a reachability plot which can help in the interpretation of creating clusters.

BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies): – It creates clusters by generating a summary of the data. It works well with huge datasets as it first summarises the data and then uses the same to create clusters. However, it can only deal with numeric attributes that can be represented in space.

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Applications

The clustering applications are vast in nature. Precisely in data mining, clustering is used as an analysis process to deduce images, data and recognize underlying patterns in them. This helps companies to do better market research, and by using data clustering companies often discover new groups in the database of customers.
For example, in retail marketing, retail companies use the process of clustering to identify groups of household items that can be placed together to provide the customers with a more organized and put-together experience. Another example is streaming services that often perform clustering analysis to identify viewers who have similar behavior and viewing choices. In sports science as well, clustering plays an important role. Data scientists who work for sports teams often use the clustering method to identify players with similar traits and characteristics. They then group these players together to build a more efficient team.
Health insurance companies also utilize the clustering method. Actuaries at these companies collect data on various subjects such as total number of doctor visits, tidal household size, number of chronic patients in the household, the average age of household, etc, and then use this information into a clustering algorithm and set monthly premiums accordingly.

Segmentation of consumer base in the market.
Analysis of Social network.
Image segmentation.
Recommendation Systems.

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One of the famous applications of the clustering algorithms is Netflix recommendation systems. Though the company is quite subtle with its algorithms, it is validated that there are nearly 2,000 clusters or communities that share common audiovisual tastes.

For example, Cluster 290 includes people who like the series “Black Mirror”, “Lost”, and “Groundhog Day”. These clusters help Netflix to improve its knowledge of the interests of viewers and therefore make better decisions in the development of new original series.

Clustering vs Classification: Table of Differences

Even though both classification and clustering are used for categorizing objects, there is a significant difference between classification vs clustering. The difference between clustering and classification can be categorized into multiple segments such as its functionality, the process that they follow, and their complexity. Therefore, knowing classification vs clustering is crucial so that one can know when to implement each.
Let's discuss the differences between classification and clustering with examples.

Parameters	Classification	Clustering
Type of learning	Classification is a supervised machine learning technique.	Clustering is an unsupervised machine learning technique.
Training data	Classification requires labeled training data, where each data point is assigned a class label.	Clustering does not require labeled training data.
Learning goal	Data can be categorized into predetermined classes or labels using this technique.	Related data points are grouped in a cluster using this technique.
Algorithm output	The output of a classification model is a discrete class label or category.	The output of a clustering algorithm is a set of clusters.
Interpretability	Classification models generally offer clear predictions with features that are easy to interpret.	Clustering might generate clusters that are challenging to interpret, particularly in high-dimensional spaces.
Algorithm usage	Classification is ideally used for predictive modeling	Clustering is used for exploratory data analysis and identifying inherent structures or patterns within the data
Performance on large dataset	For large datasets, classification algorithms may be computationally intensive	Clustering algorithms can handle large datasets efficiently.
Performance metrics	Performance of a classification model is evaluated using metrics such as accuracy, precision, recall, and F1 score.	Performance of clustering model is evaluated using metrics such as cluster cohesion, separation, and silhouette score.
Examples of algorithm type	Examples of classification algorithms include logistic regression, decision trees, random forests, and support vector machines (SVM).	Examples of clustering algorithms include K-means, hierarchical clustering, and DBSCAN.
Examples of algorithm usage	Classification algorithms are useful for tasks like identifying whether an email is spam or not, identifying whether a customer is likely to default in credit card payment.	Clustering algorithms are useful for tasks like grouping customers based on purchasing behavior, segmenting news articles into topics.

Clustering vs Classification: Table of Differences: Detailed Comparison

Type: – Clustering is an unsupervised learning method whereas classification is a supervised learning method.
Process: – In clustering, data points are grouped as clusters based on their similarities. Hence, here the instances are classified based on their resemblance and without any class labels. Classification involves classifying the input data as one of the class labels from the output variable. Therefore, it can be defined as an approach to classifying the input instances based on their related class labels.
Prediction: – Classification involves the prediction of the input variable based on the model building. Clustering is generally used to analyze the data and draw inferences from it for better decision making.
Splitting of data: – Classification algorithms need the data to be split as training and test data for predicting and evaluating the model. Clustering algorithms do not need the splitting of data for its use.
Data Label: – Classification algorithms deal with labeled data whereas clustering algorithms deal with unlabelled data.
Stages: – Classification process involves two stages – Training and Testing. The clustering process involves only the grouping of data.
Complexity: – As classification deals with a greater number of stages, the complexity of the classification algorithms is higher than the clustering algorithms whose aim is only to group the data.
Meaning: – The major classification and clustering difference is based on their key concept. The process of classifying the input instances depending on their corresponding class labels is called classification. On the other hand, grouping the instances depending on their similarity without using class labels is called clustering.
Example Algorithms: -The examples of classification algorithms include Logistic regression, Support vector machines, Naive Bayes classifier, etc. Examples of clustering algorithms include-means clustering algorithm, Gaussian (EM) clustering algorithm, Fuzzy c-means clustering algorithm, etc.

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Applying clustering to your Business

In addition to the application of classification and clustering in data mining, you must know some of their other applications. You can apply a clustering algorithm to help reach your business goals. Moreover, you can use cluster analysis to divide and profile your customer base. Moreover, you can group shoppers based on variables that are aligned with your business objectives like performance data, demographics, or behavioral characteristics.

It can be presumed that shoppers who belong to the same cluster demonstrate the same consumer behavior. Thus, you can identically target them. Consequently, this allows you to comprehend your target market and provide the right products at the right place, time, and price.

You can use a clustering algorithm in the assortment planning and space allotment functions. After understanding every cluster, you can develop specialized customer-focused product ranges. The corresponding information is useful in the distribution of floor and shelf space, owing to the customers’ requirements in the cluster. Also, the information is useful in the succeeding assortment plan that you may have previously created.

Just like classification and clustering in machine learning provides outstanding benefits, they also benefit other sectors. For example, a clustering algorithm can help you explore the data set and search for artifacts. This can be accomplished by clustering the data and determining whether the clusters agree with the signals that one anticipates to be the dominating ones, or if they correspond to batch effects or some other technical artifacts.

Similarities Between Clustering and Classification

Although classification vs clustering in data mining have distinct differences in their applications, there are indeed certain similarities shared between the two techniques. Both classification and clustering are part of the machine learning landscape that involves training algorithms on data to generate predictions or gain insights. Both classification and clustering have the same process which involves recognizing patterns and grouping data points according to similarities. While classification and clustering algorithms may differ in terms of interpretability, both are used in data exploration and analysis to identify underlying patterns, relationships, or trends in datasets. Visualization tools such as scatter plots, heatmaps, and dendrograms can help in understanding these patterns and relationships.

It may be necessary to perform data preprocessing steps such as feature scaling, normalization, and addressing missing values before using classification or clustering methods. Both classification and clustering may require preprocessing steps to clean and prepare the data before applying the algorithms. This could include handling missing values, encoding categorical variables, and scaling features. Feature engineering techniques may be employed in both the type of algorithms to create new features or transform existing ones to improve model performance or clustering quality.

Choosing Between Clustering and Classification

The key determinant in selecting between clustering vs classification hinges on the type of learning involved. When there are available values for the target variable, it constitutes a supervised learning task, whereas the absence of such values denotes an unsupervised learning task. Classification is employed in supervised learning scenarios, while clustering is integral to unsupervised learning approaches.

The subsequent consideration in deciding between the two options involves grasping the objective of our analysis. When our aim is to forecast binary class labels such as spam or non-spam, fraud or non-fraud, or multi-class labels like the type of fruit, identifying the correct character, etc., we can utilize classification models. Conversely, if our objective is to reveal concealed patterns or groups within the dataset such as customer segmentation, detecting anomaly, and pattern recognition, clustering algorithms can be employed.

Conclusion

Clustering and classification work differently and give different results. Both are important for solving different problems. This article introduces the basics of clustering vs classification.

Clustering and Classification are important for improving how businesses work. Even though they might seem similar, they actually help us understand customers in different ways, which makes shopping better. Using clustering and classification in machine learning, we can understand and target customers better, which helps businesses make more money.

Learning about different types of algorithms and how they’re used in real life has been interesting. But it’s important to know that there are lots of other algorithms for solving problems in clustering vs classification.

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