clusterkmeans           package:goCluster           R Documentation

_C_l_u_s_t_e_r_s _a _d_a_t_a_s_e_t _w_i_t_h _t_h_e _k_m_e_a_n_s _f_u_n_c_t_i_o_n

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

     This function is used in the goCluster framework to cluster a
     dataset with the kmeans function.

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

     clusterkmeans(dataset, clusters, repeats)

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

 dataset: The dataset to be clustered. This has to be a matrix. 

clusters: This specifies the number of clusters that the dataset should
          be partitioned into. 

 repeats: It may be useful to repeat the clustering in order to get an
          impression of the variability of the clustering result. This
          option specifies the number of repeats. 

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

     Kmeans clustering will partition the dataset of the parent object
     into the number of clusters specified by the user. This will be
     repeated as often as specified in the repeats option. The class
     will return a list of groups that can subsequently be analyzed by
     statistical means for any enrichment of functional categories. The
     repetition of clustering is included since kmeans is no
     deterministic procedure. Depending on the initialization
     conditions the result may vary slightly. Thus repeating the
     process yields an average.

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

     A "tree" (list of lists) of clusters. The first level will hold as
     many list elements as the number of times the clustering has been
     repeated. Each of these elements holds a number of lists equal to
     the number of clusters requested .Each of node on this second
     level hold the unique ids of the genes in the cluster.

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

     Gunnar Wrobel, <URL: http://www.gunnarwrobel.de>.

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

     'clusterAlgorithmClara-class' 'clara'

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

     require(cclust)

     ## Get the benomyl setup
     data(benomylsetup)

     ## Extract a fraction of the dataset
     benomyldata <- benomylsetup$data$dataset[1:200,]
     benomylids  <- benomylsetup$data$uniqueid[1:200]

     ## Cluster the dataset
     clusterkmeans(exprs(benomyldata), 4, 2)

