Classification vs Clustering

Classification and clustering are techniques in machine learning that are used to classify and cluster data. These two techniques are widely used in real life for various purposes such as detecting spam and analyzing customer behavior. Also, classification and clustering are based on different types of machine learning, supervised and unsupervised. In this article, we will discuss what classification is, what clustering is, with examples, working, applications, and differences between classification and clustering.

Table of Contents:

• What is Classification?
• What is Clustering?
• Difference Between Clustering and Classification
  • Type
  • Data Labels
  • Goal
  • Output
  • Complexity
• Conclusion

What is Classification?

Classification is a task in machine learning where the goal is to assign predefined labels or categories to new observations based on the trained data. In a simple way, we can say that classification is a way of teaching a machine to recognize the patterns in labeled data so that it can predict the correct label for new and unseen data. It is a type of supervised machine learning. In classification, each part of the training data has both the inputs (features) and output labels (correct categories).

How Classification Works

The classification basically works through a cycle of learning from the labeled data, evaluating the performance on unseen data, and then predicting the correct labels for new data. 

Here is a step-by-step description of how the classification works:

• Collect the data that has inputs and labels.
• Then, prepare the data by cleaning, normalizing, and splitting it into training and testing sets.
• Now, choose a classification algorithm to use on the data for classification.
• Then, train the model using the training data.
• After training the model, evaluate the model using the test data to check how accurately it predicts the labels.
• Now, make the predictions by giving the trained model new data without labels.
• If needed, improve the model by adjusting settings and using new algorithms using more data.

Types of Classification 

Here are the main types of classification in machine learning.

1. Binary Classification

Binary classification is a type of classification where the model predicts one of two distinct classes. It is used for disease diagnosis, fraud detection, etc. 

Example: Predicting whether an email is spam or not.

2. Multiclass Classification

The multiclass classification is a type of classification in which the model predicts from three or more categories. It is used for handwritten digit recognition, document topic classification, and plant species detection, etc. 

Example: Classifying an image as a cat, dog, or rabbit.

3. Multilabel Classification

The multilabel classification is a type of classification in which the model assigns multiple labels to each input at the same time. It is used in music genre classification, movie tagging, social media post categorization, etc.

Example: A news article might be classified as politics, economy, and international.

4. Imbalanced Classification

Imbalanced classification is a type of classification in which one class appears more frequently than the others in the dataset. It is used for rare disease detection, anomaly detection, predicting equipment failure, etc. 

Example: In fraud detection, genuine transactions are far more common than fraudulent ones.

Applications of Classification

• Classification is used by email spam filters to detect whether an email is spam or not. 
• Doctors use classification models to predict if a patient has a particular disease based on test results. 
• To detect fraud in transactions, banks and payment systems use classification.
• For sentiment analysis, to classify the customer reviews as positive, negative, or neutral classification is used.
• Classification is used in image recognition systems to classify the images as animals, vehicles, or people.
• Classification is also used in face recognition systems to identify faces in photos or videos.

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What is Clustering?

Clustering is a task that aims to group data based on its characteristics, without any prior labels. In a simple way, we can say that it is the task of searching for a natural structure in a data set by dividing it into groups called clusters. Within these clusters, items in any cluster are more similar to each other within their cluster than they are in other clusters. Clustering is a type of unsupervised machine learning.

How Clustering Works

Here is a step-by-step description of how clustering works.

• Collect the data that has to be grouped or analyzed.
• Then, select the features that describe each data point.
• Now, choose a clustering algorithm to use on the data for clustering.
• At this point, indicate the number of clusters if applicable, and let the machine learning algorithm explore the data and put similar data together. 
• Now every data point belongs to the cluster it associates most closely with. 
• Now you may evaluate or investigate the clusters for the patterns or characteristics of the groups.

Types of Clustering

Here are the main types of clustering in machine learning.

1. K-Means Clustering

K-means clustering is a clustering method that divides the data into clusters that do not overlap. In this type of clustering, each data point belongs to only one cluster. 

Example: Grouping customers into 3 segments based on their income and spending.

2. Hierarchical Clustering

Hierarchical clustering is a clustering method that creates a tree-like structure called a dendrogram from either merging or splitting clusters. It is a type of clustering that is agglomerative (bottom up) or divisive (top down).

Example: Grouping species based on generic similarity. 

3. Density-Based Clustering

Density-based clustering is a type of clustering that forms clusters based on dense regions of data and separates the noise and sparse areas. It is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

Example: Detecting unusual patterns in credit card transactions.

4. Fuzzy Clustering 

Fuzzy clustering is a type of clustering that allows data points to belong to multiple clusters with different degrees of membership. It is based on the Fuzzy C-means. 

Example: A document that belongs to both science and technology topics in parts.

Applications of Clustering

• Clustering is used by businesses to segment customers based on their behaviour, such as buying patterns or spending.
• The search engines use clustering to group web pages or search results for better organization.
• Healthcare professionals use clustering to identify patterns in patient data and group similar medical conditions.
• To detect unusual or fraudulent transactions, banks and payment systems use clustering.
• Clustering is used by retailers to group products that are often bought together by customers.
• Social networking platforms also use clustering to find the communities and friend groups based on connections and connections.

Difference Between Clustering and Classification

Here are the main differences between clustering and classification in machine learning.

1. Type

Classification is a type of supervised learning in which the model is trained using the labeled data to classify new and unseen data. On the other hand, clustering is a type of unsupervised learning in which the model is trained on unlabeled data to form clusters of similar data that have no labels.

Example:

In classification, you have to teach the model about what “spam” and “not spam” emails look like, while in clustering, the model is trained, and it analyzes and finds out the clusters with similar features to its own.

2. Data Labels

Classification uses labeled data, thus, each data comes with a known output or category. On the other hand, clustering uses unlabeled data, so the model finds clusters without any predefined categories.

Example:

In classification, a training dataset might have emails marked as “spam” and “not spam”, and in clustering, the training dataset has only emails that are not marked as “spam” or “not spam”, so the model will group similar emails on its own.

3. Goal

The goal of classification is to classify the correct category or label for new and unseen data based on the trained dataset. While the goal of classification is to form clusters of similar hidden patterns in the dataset without using any labels.

Example:

The classification will check whether new emails are spam or not on the basis of the trained examples, and the clustering will group similar emails together without checking which are spam and which are not.

4. Output

Classification gives a specific class label for each data point as an output, while clustering gives a cluster ID or group number to show which group the data belongs to without any labels.

Example:

In classification, the result can be “This email is spam”, and in clustering, the result might be “This email belongs to Cluster 2”.

5. Complexity

Classification is generally less complex when labels are available because the model learns directly from the already known examples. While clustering is more complex because the model has to analyze the data structure and decide how many clusters exist on its own.

Example:

In classification, the model simply learns to separate the cats from dogs based on the labeled images, and in clustering, the model looks at the features such as fur, size, and shape and then groups the cats and dogs.

Here is the difference table between classification and clustering for a more precise description.

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Conclusion

Classification and clustering are both important tasks in machine learning, with different purposes. Classification is a supervised learning method, and clustering is an unsupervised learning method. Both methods are used in real life for classifying and grouping up dataset. Thus, understanding both classification and clustering helps in making more intelligent systems that can make more accurate decisions and give more accurate results from the new, raw, and unseen data.

Classification vs. Clustering – FAQs