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Hierarchical (Agglomerative) Clustering via stats

Source: R/hier_clust_stats.R
details_hier_clust_stats.Rd

hier_clust() creates Hierarchical (Agglomerative) Clustering model.

Details

For this engine, there is a single mode: partition

Tuning Parameters

This model has 1 tuning parameters:

  • num_clusters: # Clusters (type: integer, default: no default)

Translation from tidyclust to the original package (partition)

hier_clust(num_clusters = integer(1)) %>% 
  set_engine("stats") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

## Hierarchical Clustering Specification (partition)
## 
## Main Arguments:
##   num_clusters = integer(1)
##   linkage_method = complete
## 
## Computational engine: stats 
## 
## Model fit template:
## tidyclust::.hier_clust_fit_stats(data = missing_arg(), num_clusters = integer(1), 
##     linkage_method = "complete")

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), tidyclust will convert factor columns to indicators.

References

  • Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole. (S version.)

  • Everitt, B. (1974). Cluster Analysis. London: Heinemann Educ. Books.

  • Hartigan, J.A. (1975). Clustering Algorithms. New York: Wiley.

  • Sneath, P. H. A. and R. R. Sokal (1973). Numerical Taxonomy. San Francisco: Freeman.

  • Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press: New York.

  • Gordon, A. D. (1999). Classification. Second Edition. London: Chapman and Hall / CRC

  • Murtagh, F. (1985). “Multidimensional Clustering Algorithms”, in COMPSTAT Lectures 4. Wuerzburg: Physica-Verlag (for algorithmic details of algorithms used).

  • McQuitty, L.L. (1966). Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825–831. doi:10.1177/001316446602600402.

  • Legendre, P. and L. Legendre (2012). Numerical Ecology, 3rd English ed. Amsterdam: Elsevier Science BV.

  • Murtagh, Fionn and Legendre, Pierre (2014). Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of Classification, 31, 274–295. doi:10.1007/s00357-014-9161-z.