The key difference between clustering and classification is that clustering is an unsupervised learning technique that groups similar instances on the basis of features whereas classification is a supervised learning technique that assigns predefined tags to instances on the basis of features.
Though clustering and classification appear to be similar processes, there is a difference between them based on their meaning. In the data mining world, clustering and classification are two types of learning methods. Both these methods characterize objects into groups by one or more features.
1. Overview and Key Difference
2. What is Clustering
3. What is Classification
4. Side by Side Comparison – Clustering vs Classification in Tabular Form
What is Clustering?
Clustering is a method of grouping objects in such a way that objects with similar features come together, and objects with dissimilar features go apart. It is a common technique for statistical data analysis for machine learning and data mining. Exploratory data analysis and generalization is also an area that uses clustering.
Clustering belongs to unsupervised data mining. It is not a single specific algorithm, but it is a general method to solve a task. Therefore, it is possible to achieve clustering using various algorithms. The appropriate cluster algorithm and parameter settings depend on the individual data sets. It is not an automatic task, but it is an iterative process of discovery. Therefore, it is necessary to modify data processing and parameter modeling until the result achieves the desired properties. K-means clustering and Hierarchical clustering are two common clustering algorithms in data mining.
What is Classification?
Classification is a categorization process that uses a training set of data to recognize, differentiate and understand objects. Classification is a supervised learning technique where a training set and correctly defined observations are available.
The algorithm that implements classification is the classifier whereas the observations are the instances. K-Nearest Neighbor algorithm and decision tree algorithms are the most famous classification algorithms in data mining.
What is the Difference Between Clustering and Classification?
Clustering is unsupervised learning while Classification is a supervised learning technique. It groups similar instances on the basis of features whereas classification assign predefined tags to instances on the basis of features. Clustering split the dataset into subsets to group the instances with similar features. It does not use labelled data or a training set. On the other hand, categorize the new data according to the observations of the training set. The training set is labelled.
The goal of clustering is to group a set of objects to find whether there is any relationship between them, whereas classification aims to find which class a new object belongs to from the set of predefined classes.
Summary – Clustering vs Classification
Clustering and classification can seem similar because both data mining algorithms divide the data set into subsets, but they are two different learning techniques, in data mining to get reliable information from a collection of raw data. The difference between clustering and classification is that clustering is an unsupervised learning technique that groups similar instances on the basis of features whereas classification is a supervised learning technique that assigns predefined tags to instances on the basis of features.
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