dunn                 package:clValid                 R Documentation

_D_u_n_n _I_n_d_e_x

_D_e_s_c_r_i_p_t_i_o_n:

     Calculates the Dunn Index for a given clustering partition.

_U_s_a_g_e:

     dunn(distance = NULL, clusters, Data = NULL, method = "euclidean")

_A_r_g_u_m_e_n_t_s:

distance: The distance matrix (as a matrix object) of the clustered
          observations.  Required if 'Data' is NULL.

clusters: An integer vector indicating the cluster partitioning

    Data: The data matrix of the clustered observations. Required if
          'distance' is NULL.

  method: The metric used to determine the distance matrix.  Not used
          if 'distance' is provided.

_D_e_t_a_i_l_s:

     The Dunn Index is the ratio of the smallest distance between
     observations not in the same cluster to the largest intra-cluster
     distance.  The Dunn Index has a value between zero and infinity,
     and should be maximized. For details see the package vignette.

_V_a_l_u_e:

     Returns the Dunn Index as a numeric value.

_N_o_t_e:

     The main function for cluster validation is 'clValid', and users
     should call this function directly if possible.

_A_u_t_h_o_r(_s):

     Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta

_R_e_f_e_r_e_n_c_e_s:

     Dunn, J.C. (1974). Well separated clusters and fuzzy partitions.
     Journal on Cybernetics, 4:95-104.

     Handl, J., Knowles, K., and Kell, D. (2005). Computational cluster
     validation in post-genomic data analysis. Bioinformatics 21(15):
     3201-3212.

_S_e_e _A_l_s_o:

     For a description of the function 'clValid' see 'clValid'.

     For a description of the class 'clValid' and all available methods
     see 'clValidObj' or 'clValid-class'.

     For additional help on the other validation measures see 'dunn',
     'stability',  'BHI', and 'BSI'.

_E_x_a_m_p_l_e_s:

     data(mouse)
     express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
     rownames(express) <- mouse$ID[1:25]
     ## hierarchical clustering
     Dist <- dist(express,method="euclidean")
     clusterObj <- hclust(Dist, method="average")
     nc <- 2 ## number of clusters      
     cluster <- cutree(clusterObj,nc)
     dunn(Dist, cluster)

