hc                  package:mclust                  R Documentation

_M_o_d_e_l-_b_a_s_e_d _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:

     Agglomerative hierarchical clustering based on maximum likelihood
     criteria  for MVN mixture models parameterized by eigenvalue
     decomposition.

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

     hc(modelName, data, ...)

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

modelName: A character string indicating the model. Possible models: 

            "E" : equal variance  (one-dimensional) 
           "V" : spherical, variable variance (one-dimensional) 
           "EII": spherical, equal volume 
           "VII": spherical, unequal volume 
           "EEE": ellipsoidal, equal volume, shape, and orientation 
           "VVV": ellipsoidal, varying volume, shape, and orientation 

    data: A numeric vector, matrix, or data frame of observations.
          Categorical variables are not allowed. If a matrix or data
          frame, rows correspond to observations and columns correspond
          to variables. 

     ...: Arguments for the method-specific hc functions. See 'hcE'. 

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

     Most models have memory usage of the order of the square of the
     number groups in the initial partition for fast execution. Some
     models, such as equal variance or '"EEE"', do not admit a fast
     algorithm under the usual agglomerative hierarchical clustering
     paradigm.  These use less memory but are much slower to execute.

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

     A numeric two-column matrix in which the _i_th row gives the 
     minimum index for observations in each of the two clusters merged
     at the _i_th stage of agglomerative hierarchical clustering.

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

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

     C. Fraley (1998). Algorithms for model-based Gaussian hierarchical
     clustering. _SIAM Journal on Scientific Computing 20:270-281_. 
     See <URL: http://www.stat.washington.edu/mclust>. 

     C. Fraley and A. E. Raftery (2002a). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_.  See <URL:
     http://www.stat.washington.edu/mclust>. 

     C. Fraley and A. E. Raftery (2002b). MCLUST:Software for
     model-based clustering, density estimation and discriminant
     analysis.  Technical Report, Department of Statistics, University
     of Washington.  See <URL: http://www.stat.washington.edu/mclust>.

_N_o_t_e:

     If 'modelName = "E"' (univariate with equal variances) or
     'modelName = "EII"' (multivariate with equal spherical
     covariances), then the method is equivalent to Ward's method for
     hierarchical clustering.

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

     'hcE',..., 'hcVVV', 'hclass'

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

     data(iris)
     irisMatrix <- as.matrix(iris[,1:4])

     hcTree <- hc(modelName = "VVV", data = irisMatrix)
     cl <- hclass(hcTree,c(2,3))

     par(pty = "s", mfrow = c(1,1))
     clPairs(irisMatrix,cl=cl[,"2"])
     clPairs(irisMatrix,cl=cl[,"3"])

     par(mfrow = c(1,2))
     dimens <- c(1,2)
     coordProj(irisMatrix, classification=cl[,"2"], dimens=dimens)
     coordProj(irisMatrix, classification=cl[,"3"], dimens=dimens)

