100 Facts About Clustering

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Here’s a list of 100 technical facts about clustering:

1. Clustering is an unsupervised machine learning technique for grouping similar data points.
2. K-means clustering aims to partition data into k clusters based on the mean of data points.
3. Hierarchical clustering builds a tree of clusters by recursively merging or splitting them.
4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters based on density.
5. Agglomerative clustering uses a bottom-up approach, starting with individual data points as clusters.
6. Divisive clustering employs a top-down approach, starting with all data points in one cluster.
7. The term “centroid” refers to the center point of a cluster in K-means clustering.
8. Dendrograms are tree-like diagrams representing hierarchical clustering relationships.
9. Silhouette score is a metric for evaluating the cohesion and separation of clusters.
10. The Davies-Bouldin Index is another metric for evaluating clustering performance.
11. The Curse of Dimensionality can affect clustering algorithms in high-dimensional spaces.
12. Euclidean distance is commonly used in distance-based clustering algorithms.