qtclust              package:flexclust              R Documentation

_Q_T _C_l_u_s_t_e_r_i_n_g

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

     Perform QT clustering on a data matrix.

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

     qtclust(x, radius, family = kccaFamily("kmeans"), control = NULL)

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

       x: A numeric matrix of data, or an object that can be coerced to
          such a matrix (such as a numeric vector or a data frame with
          all numeric columns).

  radius: Maximum radius of clusters.

  family: Object of class 'kccaFamily'.

 control: An object of class 'flexclustControl'.

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

     This function implements a generalization of the QT clustering
     algorithm by Heyer et al. (1999). The only difference is that in
     each iteration not all possible cluster start points are
     considered, but only a random sample of size 'control@ntry'. In
     most cases the resulting solutions are almost the same at a
     considerable speed increase. If 'control@ntry' is set to the size
     of the data set, the original algorithm is obtained.

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

     An object of class '"kcca"'.

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

     Friedrich Leisch

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

     Heyer, L. J., Kruglyak, S., Yooseph, S. (1999). Exploring
     expression data: Identification and analysis of coexpressed genes.
     Genome Research 9, 1106-1115.

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

     x <- matrix(10*runif(1000), ncol=2)

     ## maximum distrance of point to cluster center is 3
     cl1 <- qtclust(x, radius=3)

     ## maximum distrance of point to cluster center is 1
     ## -> more clusters, longer runtime
     cl2 <- qtclust(x, radius=1)

     opar <- par(c("mfrow","mar"))
     par(mfrow=c(2,1), mar=c(2.1,2.1,1,1))
     plot(x, col=predict(cl1), xlab="", ylab="")
     plot(x, col=predict(cl2), xlab="", ylab="")
     par(opar)

