Classification vs. Clustering- Which One is Right for Your Data?

Introduction

Imagine walking into a shopping mall with hundreds of brands and products, all jumbled up and randomly placed in the shops. Would you be able to find the desired brand or product easily? Definitely not. This is where the organization part comes in— by categorizing the brands as a whole or taking a more challenging route and grouping similar products. Once classified or clustered, finding what you’re looking for becomes much more manageable. Take the same logic to a data analysis project with many datasets. Machine learning classification and clustering techniques are used to group data points, making it possible for analysts to work around data points. While both techniques may seem similar, they are fundamentally different—they differ in their approach and methodology. This article will explore classification vs. clustering and how each technique is used in real-world applications.

Overview:

• Discover how these two fundamental techniques in data analysis differ in approach and purpose. From supervised classification to unsupervised clustering, grasp the core principles that set these methods apart.
• Uncover the distinctions between classification and clustering in data mining, including objectives, data requirements, and applications, to understand better when to use each.
• See how classification vs. clustering techniques are applied in customer segmentation, image recognition, fraud detection, and text classification, making data insights more actionable.
• Dive into various types, such as binary and multi-class classification or model-based and density-based clustering, and explore popular algorithms that bring these techniques to life.
• Based on data characteristics, learn when to apply classification or clustering. This will help you make informed decisions for accurate and insightful data analysis.

What is Classification?

Consider a library where books belonging to the same subject are grouped. For instance, all historical books are kept together; all related to science are grouped, and so on. Now imagine that the library is your data analysis project, and data is grouped based on some features (gender, location, data type, etc.)-this is called classification.

Etymologically, classification in data analysis is a process of grouping data into categories or classes based on specific criteria. Like how convenient it is to find a book based on its subject, classifying data helps you find relevant data segments and analyze them more efficiently. You can look at each group separately to find patterns or insights that may be hidden if you look at the data as a whole.

Types of Classification

Some of the most widely used types of classification in data analysis are mentioned below.

Binary Classification

This is the simplest type of classification. Here data is bifurcated into precisely two categories/classes. The most standard example would be email classification— classifying emails as spam or not spam.

Multi-class Classification

It is called multi-class classification when there are more than two classes/categories. For example, an image of a fruit might be classified into different fruit types, such as apple, banana, and orange.

Classification Algorithms

Neural Network Classification

Neural networks are complex mathematical models trained on large amounts of data to learn complex patterns and relationships. The most widely used neural networks are convolutional neural networks (CNNs), mainly used for image-based classification problems.

K-Nearest Neighbor Classification

This algorithm classifies data points based on their proximity to other data points. The class of a data point is assigned based on the class of its k-nearest neighbors.

Decision Tree Classification

This popular classification algorithm uses tree-like structures, called decision trees, where each node in the tree showcases a predetermined feature or attribute of the data. Secondly, each leaf node represents a class. The tree is constructed by recursively splitting the data based on the values of the features until a leaf node is reached.

Applications of Classification

Classification is very prominently used in data analysis, and this machine learning technology has several applications. Here are some common applications of classification.

Image and Speech Recognition

Classification algorithms are used in image and speech recognition to identify and classify images and sounds. One real-world example of how classification is used in image recognition is object recognition. Classification algorithms can be used to train machine learning models to recognize different objects within images.

Customer Segmentation

Classification algorithms can be used to segment customers into different categories based on their preferences, behavior, and demographic information. This is prominent in the retail industry, wherein retailers often use segmentation to group customers. You may wonder which suits classification vs. clustering. Here, the retailers previously define the classes/groups—- so it is a classical case of classification.

Sentiment Analysis

Classification algorithms can classify customer reviews and social media posts into positive, negative, or neutral sentiments. The most standard sentiment classification is done in social media monitoring. Companies like Twitter, Facebook, etc., use classification algorithms to classify content as positive, negative, or neutral based on its sentiment.

Email Spam Filtering

Classification algorithms can classify emails as spam or not spam based on their content and characteristics.

You must be wondering what classification vs. clustering implies, even though both are near-similar processes. Let’s learn more specifically about clustering as a data analysis process.

What is Clustering?

Like classification, clustering is a grouping technique that groups objects with the same attributes/functionalities. Put, clustering portions a dataset into smaller subsets or “clusters.” However, unlike classification, the clusters are predefined. Grouping is achieved by mapping similarities and common characteristics in real-time.

In summary, the primary difference between classification and clustering is the prior determination of class/groups/clusters.

Types of Clustering

There are a few kinds of clustering practices in data analysis. Some of them are:

Model-based Clustering

This clustering approach uses statistical models to group similar data points into clusters. The goal is to find a model to explain the observed data as a mixture of different probability distributions. The number of distributions and their parameters are estimated using a maximum likelihood or Bayesian approach.

Poisson distributions, a mixture of exponential distributions, t-distributions, and a few others can also form clusters in a model-based setting. Though, it depends on the type of data in the picture. Some of the most common applications of model-based clustering include image segmentation, gene expression analysis, and customer segmentation.

Density-based Clustering

Imagine a crowded city with people gathering in specific areas (like parks, theatres, restaurants, etc.) based on their interests. For example, foodies might gather in a neighborhood known for its restaurants.

Density-based clustering is a method in data analysis that groups data points closely packed together in high-density regions. These data points belong to the same cluster, similar to people in the same area. Data points that are isolated in areas of low density are considered as noise and not assigned to any cluster, similar to how people who are not part of any particular group.

Hierarchical Clustering

A clustering technique that results in tree-like structures of nested clusters having similar/merging data points based on their similarity. It can be either agglomerative (starting with individual data points and merging them into larger clusters) or divisive (starting with all data points as one large cluster and recursively splitting it into smaller clusters).

Examples of Hierarchical clustering include CURE (clustering using representative), BIRCH (balanced iterative reducing clustering and using hierarchies), etc.

Hierarchical clustering is widely used for biological classification, social network analysis, natural language processing, time series analysis, and many others.

Applications of Clustering

We hope that by now, you must be ahead of the classification vs clustering in data mining debate. Read on to see how clustering is applicable in the real world.

Anomaly Detection

Clustering can find odd or abnormal data patterns. This information can then be used for fraud detection, network intrusion detection, or predictive maintenance. For instance, machine learning-based clustering is used to detect fraud in credit card transactions, a significant challenge for financial institutions.

Biological Classification

Clustering can classify genes or proteins into functional categories based on their expression patterns or sequence similarities. This information can then be used to identify possible therapeutic targets or illness biomarkers.

For instance, the famous PAM50 clustering algorithm is used in gene analysis to identify and analyze the genetic information that synthesizes proteins.

Social Network Analysis

Clustering can be used to group individuals in a social network based on their patterns of interaction. Here, social networks are mapped and arranged in nodes (these nodes could be people, personalities, or other entities). The edges or interlinks that connect the nodes represent their relationships or interactions. This information can then identify key influencers or communities within the network.

Document Clustering

Clustering algorithms are widely used to group documents into classes/clusters based on the similarity of certain words, topics, or other features. Further, it can be used to group documents with similar sentiments, which can be useful to identify trends or predict consumer behaviour. Additionally, you can use the clustering technique for topic modeling in content analysis.

What is the Difference Between Classification and Clustering?

In classification, the goal is to assign predefined classes or labels to instances based on their features. It involves supervised learning and requires labeled data for training. The output of classification is the class or label assignment.

In clustering, the objective is to group instances that share similarities without predefined classes or labels. It is an unsupervised learning task and does not require labeled data. The clustering output is the cluster assignments, which help identify patterns or structures in the data.

These differences in objectives, purposes, supervision, training, output, and examples distinguish classification and clustering as two distinct approaches to data analysis.

Similarities Between Classification and Clustering

While there is a difference between classification and clustering in machine learning, there are also a few similarities. For starters, both techniques are used in data analysis and machine learning. Some other similar points are mentioned below.

• When we look at the primary structure of both processes, they are almost identical—both group data into classes/clusters.
• Both classification and clustering can be used to gain insights and make predictions about new data.
• Both classification and clustering require a careful selection of features to be analyzed.

Choosing Between Classification and Clustering

Making a conscious decision between machine learning clustering vs. classification depends on a few factors. Read on to learn more.

When you have labeled data, you can opt for a supervised classification algorithm, as it works best when you already know the input data and potential outcomes of the process. For example, you can choose classification when you know the kind of customer data you have and wish to segregate them to decide which services or products they prefer.

On the other hand, if you have only a common input dataset (i.e., unlabeled data), you should prefer clustering as it involves obtaining information about the input data without any assumptions about the outcome. For example, you’re in a social service company wishing to develop policies. For this situation, you just have a standard dataset (the whole population) and wish to identify and cluster groups with common characteristics.

Conclusion

After going through this blog, you will realize that the classification vs. clustering debate is only valid because of the different types of data (labeled or unlabeled) and approaches in machine learning. Both are powerful techniques used in data analysis to group data points based on similarities. Diving deeper into the differences between classification and clustering is essential for selecting the appropriate technique for a given problem and ensuring accurate results.

Analytics Vidhya (AV) is the right place for you to explore clustering and classification more as machine learning techniques. The platform offers various courses and tutorials on machine learning, artificial intelligence, data science, and analysis, including classification algorithms, clustering techniques, and data preprocessing. The focus is on how AI and ML can help develop and improve these areas. As a modern-day tech enthusiast, you can check out their AI and ML Blackbelt program with one-on-one mentorship to understand how these technologies give rise to augmented analytics.

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