Don’t Classification and Clustering have more similarities than differences? Aren’t both about characterizing objects into groups by one or more features?
Classification is about classifying data into predefined categories (defining whether a patient record is associated with a specific disease), while Clustering is about grouping data looking similar to a set of groups (grouping patients’ records with similar symptoms, without knowing what they indicate).
While Classification may tell a telecom company whether a region needs the network to be enhanced by classifying all the areas according to their signals, and defining which one has a bad connection, Clustering helps to define the location of the tower where it should be installed, by dividing the region into clusters, taking into consideration the maximum range the cell tower would have to provide good connectivity to all users of this area.
In Classification, supervised learning having two phases (training phase and test phase), a labeled object is classified into a predefined category, based on training data being already classified itself into different categories. Ex. Define whether a Facebook comment is positive or negative based on specific criteria.
On the other hand, in Clustering, unsupervised learning having one phase (training data divided into clusters), the data is divided into unknown beforehand groups, according to the similarity of the objects. The less the distance between two objects (data points), the more they tend to be similar. As no predefined categories here, the data is provided to the algorithm that decides according to which criteria this data should be divided. Ex. Divide Facebook posts by themes (fashion, tech, etc.) based on the content of the post.
As long as the data is labeled, Classification is the learning method to determine the class to which a data point should belong. Once the data is unlabeled, Clustering is the learning method to verify the similarity/dissimilarity existing in it, to be grouped.
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