

mhtree(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_.

_U_s_a_g_e_:

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

_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 infor-
     mation relating to the clustering process.

_D_E_S_C_R_I_P_T_I_O_N_:

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

_N_O_T_E_S_:

     Only the six models illustrated in the example below
     are supported at present.  These correspond to the mod-
     els 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.  Dif-
     ferent 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 Hierar-
     chical 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")

