mhtree                package:mclust                R Documentation

_C_l_a_s_s_i_f_i_c_a_t_i_o_n _T_r_e_e _f_o_r _M_o_d_e_l-_b_a_s_e_d _G_a_u_s_s_i_a_n _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r_i_n_g.

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

     Determines a classification tree for agglomerative hierarchical
     clustering using criteria based on parameterizations of Gaussian
     mixture models that reflect the underlying geometry of the
     resulting clusters.

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

     mhtree(data, modelid, partition, min.clusters = 1, verbose = F, ...)
     print.mhtree(tree.object, ...)

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

    data: matrix of observations. 

 modelid: An integer specifying a parameterization of the MVN
          covariance matrix defined  by volume, shape and orientation
          charactertistics of the underlying clusters.  The allowed
          values or `model' and their interpretation are as follows:
          `"EI"' : uniform spherical, `"VI"' : spherical, `"EEE"' :
          uniform variance,  `"VVV"' : unconstrained  variance, `"EFV"'
          : fixed (user supplied) uniform volume, `"VFV"' : fixed (user
          supplied) shape. 

partition: initial classification of the data. The default puts every
          observation in a singleton cluster. 

min.clusters: minimum number of clusters desired. The default is to
          carry out agglomerative hierarchical clustering until
          termination, that is, until all observations belong to a
          single group. 

 verbose: A logical variable specifying printing of the model type when
          set to `T'. 

     ...: Allows users to specify the required `shape' parameter for
          the two fixed shape models `"EFV"' and `"VFV"', and to change
          default parameters that are  used in the algorithms
          underlying some models. 

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

     an object of class `"mhclust"', which consists of a classification
     tree with attributes giving other information relating to the
     clustering process.

_N_O_T_E_S:

     Only the six models illustrated in the example below are supported
     at present. These correspond to the models discussed in the
     Banfield and Raftery  reference. It may be desirable to transform
     the data in some way before attempting to  partition it into
     clusters. Different permuations of the data may produce different
     classifications, because `mhclust' resolves ties in a way that is
     dependent on the order of the observations, and because values of
     criterion that are close may change enough to affect the choice of
     merge pairs in a given stage.

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

     J. D. Banfield and A. E. Raftery, Model-based Gaussian and
     non-Gaussian Clustering, Biometrics, 49:803-821 (September 1993).

     C. Fraley, Algorithms for Model-based Gaussian Hierarchical
     Clustering, Technical Report No. 311, Department of Statistics,
     University of  Washington (October 1996), to appear in SIAM
     Journal on Scientific Computing.

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

     `mhclass', `loglik', `awe', `partuniq'

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

     data(iris)

     # Ellipsoidal, equal volume, shape and orientation
     mhtree(iris[,1:4], modelid = "EEE")

     # Spherical, equal volume, fixed shape, variable orientation
     shape <- c(1,1/2,1/3,1/4)
     mhtree(iris[,1:4], modelid = "EFV", shape=shape)

     # Spherical, equal volume (Ward's method).
     mhtree(iris[,1:4], modelid = "EI")

     # Ellipsoidal, equal volume, constant shape, variable orientation
     mhtree(iris[,1:4], modelid = "VFV", shape=shape)

     # Spherical, variable volume
     mhtree(iris[,1:4], modelid = "VI")

     # Unconstrained (default).
     mhtree(iris[,1:4], modelid = "VVV")

