Top 3 Aspects of AI Clustering

The following are the top three aspects of AI clustering:

1. Algorithm Selection: Choosing a clustering algorithm is crucial. Different algorithms, such as K-means, DBSCAN, and hierarchical clustering, have strengths which are suited to different types of data and clustering objectives.
2. Feature Engineering: Clustering results relies on the features used. Effective feature engineering is selecting and transforming the correct variables to capture the underlying patterns in the data.
3. Evaluation Metrics: Assessing the performance of clustering algorithms is an unsupervised learning task. Evaluation metrics include silhouette score, Davies-Bouldin index, and within-cluster sum of squares.