clusterSim

Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (doi:10.1007/BF02294245, doi:10.1007/BF01908075, doi:10.1080/01621459.1971.10482356, doi:10.1007/978-3-642-57280-7_11, doi:10.1007/BF01897163, doi:10.1007/978-3-642-55721-7_12, doi:10.1109/TPAMI.1979.4766909, doi:10.1080/03610927408827101, doi:10.1080/01621459.1974.10480191, doi:10.1111/1467-9868.00293, doi:10.1207/S15327906MBR3502_5, doi:10.1007/978-3-540-78246-9_11)

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