Clustering vs Classification Explained With Examples

We use classification and clustering algorithms in machine learning for supervised and unsupervised tasks respectively. In this article, we will discuss clustering vs classification in machine learning to discuss the similarities and differences between the two tasks using examples.

What is Clustering in Machine Learning?

Clustering is an unsupervised machine-learning task. In clustering, we try to group together similar data points in a given dataset based on their features or characteristics. Here, we have no prior knowledge of the class labels or categories of the data points. In simple terms, clustering is the process of partitioning a dataset into clusters or groups of data points with similar properties. In clustering, we first group the training data points into different clusters. Then, we can assign cluster labels to new data points based on their similarities with the existing clusters.

To understand this, consider a dataset containing information about customers, such as age, gender, income, and spending habits. With clustering, we can group customers with similar characteristics together to better understand their behavior. After making clusters, we can analyze each cluster and label them with categories such as “loyal customers” or “new customers.” Finally, if we need to label a new cluster, we can use the existing cluster labels and data points.

There are various types of clustering algorithms, including k-means clustering, DBSCAN, hierarchical clustering, Gaussian mixture models, k-modes clustering, and k-prototype clustering among others. Each clustering algorithm finds patterns in the data without any supervision. After creating clusters, it is the role of the data analyst or data scientist to interpret and make sense of the results. The choice of algorithm depends on the specific problem and the characteristics of the data.

What is Classification in Machine Learning?

Classification is a supervised learning approach used in machine learning tasks. In classification, we are given a dataset containing labels for each data point and the aim of the classification process is to assign a class label to a new input data point based on a set of training examples. We can say that classification is the process of categorizing new data points into predefined classes or categories using an existing training dataset.

To understand this, consider that we have a dataset containing information about emails, such as sender, subject, and content, and each email is labeled as spam or not spam. In the classification task, we will build a model that can predict whether a new, unseen email is spam or not spam based on its characteristics and the available dataset.

There are various types of classification algorithms. Some of the classification algorithms are decision trees, random forests, logistic regression, support vector machines, K-Nearest Neighbors classification, and neural networks, among others. Again, the choice of algorithm depends on the specific problem and the characteristics of the data. Each classification algorithm learns the patterns in the data from labeled examples during the training phase. Then, it uses this learning to make predictions on new and unseen data. 

Clustering vs Classification Examples

Clustering and classification algorithms are used in various tasks in industries. Following are some examples of clustering vs classification algorithms.

We can specify the following tasks as clustering processes. The process

1. Companies often use customer segmentation to group customers based on demographics, purchase behavior, or preferences.
2. Scientists use clustering to identify groups of genes with similar expression patterns in genomic data analysis.
3. Search engines often use clustering to group similar news articles together for recommendation or news aggregation purposes.
4. We can also use clustering for grouping together similar images in computer vision applications such as image recognition and object detection.
5. We can use clustering for identifying clusters of users with similar browsing behavior on a website or app for targeted advertising or content recommendations.

Just like clustering, classification algorithms also have many applications in retail, finance, marketing,  and healthcare industries. Some examples of classification in machine learning include the following tasks.

1. Banks use classification for Identifying fraudulent credit card transactions based on transaction history, purchase amount, and other factors.
2. Email service providers classify emails as spam or not spam based on the content, sender, and other attributes using classification algorithms.
3. Marketing teams use classification algorithms for identifying the sentiment of a piece of text (such as a movie review) as positive, negative, or neutral.
4. We can classify images containing a certain object or feature (such as a face or a specific object) in computer vision applications.
5. Healthcare applications use classification algorithms for predicting the outcome of a medical diagnosis or treatment based on patient data such as age, symptoms, and medical history.

When to Use Clustering vs Classification?

To decide on using clustering vs classification algorithms, we need to consider different aspects of the problem such as the available dataset, the nature of the problem, etc. Let us discuss some of the aspects to decide on when to use clustering vs classification.

1. Nature of the problem: We use clustering for exploratory data analysis and to gain insights into the data. On the other hand, classification algorithms are used to make predictions on new data. So, if you don’t have any information about the dataset, you can use clustering techniques. If you have a labeled dataset and you need to classify new data points based on existing data, we can use classification algorithms.
2. Availability of labeled data: We use clustering when the goal is to group similar data points together. On the other hand, classification is used when the goal is to assign class labels to a new data point. If we don’t have any information about the dataset and the goal is to find similarities or patterns in the data, we can use clustering. If we get a dataset with labeled data points and our goal is to predict the class label of new data points, we can use classification algorithms.

Classification vs Clustering Objective Functions

We use objective functions to determine the quality of the results produced by machine learning algorithms. Let us discuss some of the objective functions used in classification vs clustering.

Objective Functions for Classification

In classification, we use an objective function to measure how well a model is performing at predicting the correct class labels for a given set of inputs. Following are some of the objective functions used in classification algorithms.

1. Cross-entropy loss: This is a widely used objective function for classification, particularly for neural networks. Cross-entropy loss measures the difference between the predicted class probabilities and the true class probabilities and aims to minimize the average negative log-likelihood of the correct class.
2. Hinge loss: This objective function is used for linear classifiers such as support vector machines (SVMs). Hinge loss aims to maximize the margin between the decision boundary and the training examples and penalizes examples that are misclassified or lie too close to the boundary.
3. Logistic loss: Similar to cross-entropy loss, logistic loss measures the difference between the predicted class probabilities and the true class probabilities. It is commonly used in logistic regression and aims to maximize the likelihood of the correct class labels.
4. Accuracy: While accuracy is not a traditional objective function, it is often used as a performance metric for classification tasks. Accuracy measures the proportion of correct predictions made by the model and can be useful for evaluating the overall performance of the model.
5. F1 score: The F1 score is another commonly used performance metric for classification tasks, particularly when dealing with imbalanced datasets. It balances the precision and recall of the model and is calculated as the harmonic mean of these two metrics.
6. AUC-ROC: The area under the receiver operating characteristic (ROC) curve is a popular performance metric for binary classification tasks. It measures the trade-off between the true positive rate and the false positive rate and provides an overall measure of the model’s ability to distinguish between positive and negative examples.

Objective Functions For Clustering

In clustering, an objective function is used to measure how well the algorithm is able to group similar data points together and separate dissimilar ones. Here are some commonly used objective functions for clustering:

1. Within-Cluster Sum of Squares (WCSS): This is a widely used objective function for clustering, particularly for k-means clustering. It measures the total squared distance between each data point and its cluster centroid. The goal of the algorithm is to minimize the within-cluster sum of squares.
2. Silhouette Coefficient: This objective function measures the similarity of each data point to its own cluster compared to other clusters. It ranges from -1 to 1. Here, Silhouette Coefficient values close to 1 indicate that the data point is well-clustered and values close to 0 indicate that the data point is on the boundary between clusters. The values close to -1 indicate that the clusters aren’t very good.
3. Davies-Bouldin Index: This objective function measures the average similarity between each cluster and its most similar cluster, compared to the average distance between each cluster and its most dissimilar cluster. A lower value indicates better clustering.
4. Calinski-Harabasz Index: This objective function measures the ratio of the between-cluster variance to the within-cluster variance. A higher value indicates better clustering.
5. Normalized Mutual Information (NMI): This objective function measures the mutual information between the true class labels (if available) and the predicted cluster labels. A higher value indicates better clustering.
6. Entropy: This objective function measures the uncertainty or disorder within each cluster. It can be used in hierarchical clustering to determine the optimal number of clusters by looking for a point where the entropy decreases significantly.

Conclusion

In this article, we discussed different aspects of clustering vs classification with examples and theoretical concepts. To read about more machine learning concepts, you can read this article on fp-growth algorithm numerical example. You can also read this beginner’s guide on MLOps.

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!