Review Article
Monitoring Changes in Clustering Solutions: A Review of Models and Applications
Table 1
Clustering algorithms and its types.
| Class | Input parameters | Example | Performance evaluation |
| Partitioning | Number of clusters (k) | k-Means, HCL, CLARA | Dunn, Davies-Bouldin, Rand | Distance function | k-Mediod, neural gas | Calinski-Harabasz, Jaccard |
| Hierarchical | Distance function | CURE, BIRCH, ROCK | Cophenetic correlation | Linkage function | CHAMELEON | Dendrogram-based measures |
| Density-based | Epsilon | DBSCAN, LDBSCAN, OPTICS | Density reachability F, CSI | Minimum points | DENCLUE, STDBSCAN | Adjusted mutual information |
| Grid-based | No. of grid cells | STING, CLIQUE | Grid quality index, grid entropy | The wavelet | WaveCluster | Grid dispersion, grid purity |
| Model-based | No. of components | GMM | Akaike information criterion | Model parameters | Bayesian information criterion |
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