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Major clustering approaches
Short answer
Major approaches: partitioning (e.g., k-means), hierarchical (aglomerative/divisive), density-based (DBSCAN), grid-based (STING), model-based (EM). Hierarchical builds a dendrogram without prespecifying k; partitioning divides data into k clusters iteratively, requires k, is faster, and typically finds spherical clusters.
Describe major clustering approaches. Differentiate between hierarchical and partitioning clustering technique.
Major clustering approaches are:
- Partitioning methods: divide n objects into k clusters, then improve the partition by moving objects between clusters. Distance-based, find spherical clusters, need k. Examples: k-means, k-medoids (PAM), CLARA.
- Hierarchical methods: create a tree (dendrogram) of clusters.
- Agglomerative (bottom-up, AGNES) merges the closest clusters.
- Divisive (top-down, DIANA) splits clusters.
- Examples: BIRCH, CURE, Chameleon.
- Density-based methods: a cluster is a dense region of objects separated by sparse regions; can find arbitrary shapes and handle noise. Examples: DBSCAN, OPTICS, DENCLUE.
- Grid-based methods: quantise the space into a finite grid of cells and cluster the cells; speed independent of the number of objects. Examples: STING, CLIQUE.
- Model-based methods: assume a model (e.g. Gaussian mixture) for each cluster and fit the data to it. Examples: EM, COBWEB, SOM.
Hierarchical vs partitioning
| Basis | Hierarchical | Partitioning |
|---|---|---|
| Output | Nested clusters, dendrogram | Single flat partition |
| Number of clusters | Not required in advance | k must be given |
| Method | Merge (agglomerative) or split (divisive) | Iterative relocation |
| Reversibility | A merge/split cannot be undone | Objects can change clusters |
| Complexity | ≥ O(n²), slow | O(nkt), faster |
| Cluster shape | Can be arbitrary (single link) | Mostly spherical |
| Examples | AGNES, DIANA, BIRCH | k-means, PAM |
✓ Answer checked by Claude Opus 5.5 (high effort)
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