December 29, 2020
Often classification is confused with clustering but nothing could be further from the truth. Following are the differences between classification and clustering problems.
- Supervised vs Unsupervised: Classification problems fall under supervised learning, while clustering falls under unsupervised learning.
- Pre-defined number of Categories: In classification problems we have a predefined set of categories and they are unaffected by the choice of algorithm. On the other hand in clustering problems, the algorithm has to figure out the number of categories as well as what they are. The number of categories in clustering problems may change with the choice of algorithm.
- Anomalies: It is entirely possible in clustering problems that a new data point might not belong to any of the existing categories but not in classification problem.
by : Monis Khan
Quick Summary:
Often classification is confused with clustering but nothing could be further from the truth. Following are the differences between classification and clustering problems. Supervised vs Unsupervised: Classification problems fall under supervised learning, while clustering falls under unsupervised learning. Pre-defined number of Categories: In classification problems we have a predefined set of categories and they are […]