What are clustering techniques in data science?
An example of an AI chatbot conversation about clustering techniques in data science.
What are clustering techniques in data science?
Clustering techniques are methods used to group similar data points together based on their features.
Can you explain some common clustering algorithms?
Sure! Some common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.
How does K-means clustering work?
K-means clustering works by partitioning data into K distinct clusters by minimizing the variance within each cluster.
What are the applications of clustering in real life?
Clustering is used in various applications like customer segmentation, image recognition, and anomaly detection.
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