Clustering is a technique in machine learning and data analysis that involves grouping similar data points based on their characteristics or properties. Clustering refers to unsupervised learning tasks, so we don't need to label our data to use clustering algorithms. The goal of clustering is to identify patterns and structures in the data that may not be immediately apparent and to group data points that share common characteristics or behaviors.

Clustering is an important technique in machine learning for several reasons:

1. Pattern recognition: Clustering can help to identify patterns and structures in the data that may be difficult to detect using other techniques. By grouping similar data points, clustering can help to identify clusters of data points that share similar characteristics or behaviors.

2. Anomaly detection: Clustering can also identify outliers and anomalies in the data. By identifying data points that do not belong to any of the clusters, clustering can help to identify unusual or anomalous data points that may require further investigation.

3. Recommendation systems: Clustering can be used to build recommendation systems that suggest products, services, or content to users based on their preferences and behavior. By grouping users based on their interests or behavior, clustering can help to identify user clusters and create useful recommendations.

Which of the tasks we CAN'T solve using clustering?

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