cclust                package:cclust                R Documentation

_C_o_n_v_e_x _C_l_u_s_t_e_r_i_n_g

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

     The data given by `x' is clustered by an algorithm.

     If `centers' is a matrix, its rows are taken as the initial
     cluster centers. If `centers' is an integer, `centers' rows of `x'
     are randomly chosen as initial values.

     The algorithm stops, if no cluster center has changed during the
     last iteration or the maximum number of iterations (given by
     `iter.max') is reached.

     If `verbose' is TRUE, only for "kmeans" method, displays for each
     iteration the number of the iteration and the numbers of cluster
     indices which have changed since the last iteration is given.

     If `dist' is "euclidean", the distance between the cluster center
     and the data points is the Euclidian distance (ordinary kmeans
     algorithm). If "manhattan", the distance between the cluster
     center and the data points is the sum of the absolute values of
     the distances of the coordinates.

     If `method' is "kmeans",then we have the kmeans clustering method,
     which works by repeatedly moving all cluster centers to the mean
     of their Voronoi sets. If "hardcl" we have the On-line Update
     (Hard Competitive learning) method, which works by performing an
     update directly after each input signal, and if "neuralgas" we
     have the Neural Gas (Soft Competitive learning) method, that sorts
     for each input signal the units of the network according to the
     distance of their reference vectors to input signal.

     If `rate.method' is "polynomial", the polynomial learning rate is
     used, that means 1/t, where t stands for the number of input data
     for which a particular cluster has benn the winner so far. If
     "exponentially decaying", the exponential decaying learning rate
     is used according to par1*{(par2/par1)}^{(iter/itermax)} where
     par1 and par2 are the initial and final values of the l.rate.

     The parameters `rate.par' of the learning rate, where if
     `rate.method' is "polynomial" then by default rate.par=1.0,
     otherwise rate.par=(0.5,1e-5)

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

     cclust (x, centers, iter.max=100, verbose=FALSE, dist="euclidean",
             method= "kmeans", rate.method="polynomial", rate.par=NULL)

     print.cclust(cclust.obj)

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

       x: Data matrix

 centers: Number of clusters or initial values for cluster centers

iter.max: Maximum number of iterations

 verbose: If TRUE, make some output during learning

    dist: If "euclidean", then mean square error, if "manhattan ", the
          mean absolute error is used

  method: If "kmeans",then we have the kmeans clustering method, if
          "hardcl" we have the On-line Update (Hard Competitive
          learning) method, and if "neuralgas", we have the Neural Gas
          (Soft Competitive learning) method.

rate.method: If "kmeans", then k-means learning rate, otherwise
          exponential decaying learning rate. It is used only for the
          Hardcl method.

rate.par: The parameters of the learning rate.

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

     `cclust' returns an object of class "cclust". 

 centers: The final cluster centers.

initcenters: The initial cluster centers.

ncenters: The number of the centers.

 cluster: Vector containing the indices of the clusters where the data
          points are assigned to.

    size: The number of data points in each cluster.

    iter: The number of iterations performed.

 changes: The number of changes performed in each iteration step with
          the Kmeans algorithm.

    dist: The distance measure used.

  method: The agorithm method being used.

rate.method: The learning rate being used by the Hardcl clustering
          method.

rate.par: The parameters of the learning rate.

    call: Returns a call in which all of the arguments are specified by
          their names.

withinss: Returns the sum of square distances within the clusters.

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

     Evgenia Dimitriadou, Friedrich Leisch and Andreas Weingessel

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

     `plot.cclust', `predict.cclust'

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

     # a 2-dimensional example
     x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2),
              matrix(rnorm(100,mean=1,sd=0.3),ncol=2))
     cl<-cclust(x,2,20,verbose=TRUE,method="kmeans")
     plot(cl,x)   

     # a 3-dimensional example 
     x<-rbind(matrix(rnorm(150,sd=0.3),ncol=3),
              matrix(rnorm(150,mean=1,sd=0.3),ncol=3),
              matrix(rnorm(150,mean=2,sd=0.3),ncol=3))
     cl<-cclust(x,6,20,verbose=TRUE,method="kmeans")
     plot(cl,x)

     # assign classes to some new data
     y<-rbind(matrix(rnorm(33,sd=0.3),ncol=3),
              matrix(rnorm(33,mean=1,sd=0.3),ncol=3),
              matrix(rnorm(3,mean=2,sd=0.3),ncol=3))
              ycl<-predict(cl, y)
              plot(ycl,y)

