Clustering is used to divide data into subsets, and classification is used to create a predictive model that can be used to categorize the values of future data points. In this article, I’ll walk you through the difference between clustering and classification in machine learning.
Difference Between Clustering and Classification
Clustering is one of the types of unsupervised machine learning in which we work on an unlabeled dataset. Whereas classification is one of the categories of supervised machine learning where we deal with a labelled dataset.
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Clustering
We need to use clustering algorithms to divide the dataset into groups of data points. If you are working on a dataset that describes different types of attributes on a particular entity and your task is to group your data points based on the similarities of the attributes, in such problems we can use clustering algorithms.
Below are the most common types of clustering algorithms that we use in machine learning:
- K-Means
- DBSCAN
- Agglomerative Clustering
- BIRCH
- Mean-Shift
- Affinity Propagation
- Spectral Clustering
To summarize the use of clustering in machine learning, we need to use clustering algorithms when the conditions mentioned below are satisfied:
- You are familiar with the dataset you are using.
- Before using a machine learning algorithm, you have no idea what the clusters are in the dataset. You can’t even guess how many segments there are in the dataset before using an algorithm.
- Subsets can only be determined by one dataset that you are using.
- When your goal is to train a model that generates segments based on different types of attributes of an entity, clustering algorithms are used.
Classification
Classification algorithms are used when your problem statement is about to generate a predictive model to categorize the data points for future values. Simply put, when your goal is to train a model to predict categories of future data points, that’s the problem of classification.
Below are the most common classification algorithms that we use in machine learning:
- Decision Tree
- Naive Bayes
- K Nearest Neighbors
- Support Vector Machines
- Logistic Regression
To summarize the use of classification in machine learning, we need to use classification algorithms when the conditions mentioned below are satisfied:
- When you have a good idea of the dataset you are using.
- The categories of your dataset are defined correctly in the dataset, which means that unlike clustering, here you know the categories of the dataset before you use an algorithm.
- When the problem statement wants you to train a model using the predefined categories in the dataset to categorize future data points.
Summary
In machine learning, the clustering algorithms are used to divide the data into segments and classification algorithms are used to train models that can be used to categorize future data points. I hope you liked this article on the difference between clustering and classification in machine learning. Feel free to ask your valuable questions in the comments section below.
