When do you use classification techniques?
Here’s our quick video that explains the difference between the two and when you might use each.
Clustering and classification are machine learning methods for finding the similarities – and differences – in a set of data or documents. These methods can be used for such tasks as grouping products in a product catalog, finding cohorts of similar customers, or aggregating sets of documents by topic, team, or office.
Classifications take a set of data that you’ve already manually analyzed and labeled and uses that to train a learning model to then examine a set of new data. This is called supervised learning.
Clustering on the other hand, doesn’t require an existing data set that’s been labeled by humans but still tries to find the groupings and differences in the data. This is called unsupervised learning.
Without clustering algorithms and classification techniques, search results become watered down and non-specific. Business users and admins have to spend too much time manually adjusting relevancy and precision. Let machine learning do the work so you can focus your time and resources where they matter most.
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