hclustglad               package:GLAD               R Documentation

_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:

     Hierarchical cluster analysis on a set of dissimilarities and
     methods for analyzing it.

_U_s_a_g_e:

     hclustglad(d, method = "complete", members=NULL)

_A_r_g_u_m_e_n_t_s:

       d: a dissimilarity structure as produced by 'dist'.

  method: the agglomeration method to be used. This should be (an
          unambiguous abbreviation of) one of '"ward"', '"single"',
          '"complete"', '"average"', '"mcquitty"', '"median"' or
          '"centroid"'.

 members: 'NULL' or a vector with length size of 'd'.

_D_e_t_a_i_l_s:

     This function performs a hierarchical cluster analysis using a set
     of dissimilarities for the n objects being clustered.  Initially,
     each object is assigned to its own cluster and then the algorithm
     proceeds iteratively, at each stage joining the two most similar
     clusters, continuing until there is just a single cluster. At each
     stage distances between clusters are recomputed by the
     Lance-Williams dissimilarity update formula according to the
     particular clustering method being used.

     A number of different clustering methods are provided. _Ward's_
     minimum variance method aims at finding compact, spherical
     clusters.  The _complete linkage_ method finds similar clusters.
     The _single linkage_ method (which is closely related to the
     minimal spanning tree) adopts a `friends of friends' clustering
     strategy. The other methods can be regarded as aiming for clusters
     with characteristics somewhere between the single and complete
     link methods.

     If 'members!=NULL', then 'd' is taken to be a dissimilarity matrix
     between clusters instead of dissimilarities between singletons and
     'members' gives the number of observations per cluster. This way
     the hierarchical cluster algorithm can be ``started in the middle
     of the dendrogram'', e.g., in order to reconstruct the part of the
     tree above a cut (see examples). Dissimilarities between clusters
     can be efficiently computed (i.e., without 'hclustglad' itself)
     only for a limited number of distance/linkage combinations, the
     simplest one being squared Euclidean distance and centroid
     linkage. In this case the dissimilarities between the clusters are
     the squared Euclidean distances between cluster means.

     In hierarchical cluster displays, a decision is needed at each
     merge to specify which subtree should go on the left and which on
     the right. Since, for n observations there are n-1 merges, there
     are 2^{(n-1)} possible orderings for the leaves in a cluster tree,
     or dendrogram. The algorithm used in 'hclustglad' is to order the
     subtree so that the tighter cluster is on the left (the last, i.e.
     most recent, merge of the left subtree is at a lower value than
     the last merge of the right subtree). Single observations are the
     tightest clusters possible, and merges involving two observations
     place them in order by their observation sequence number.

_V_a_l_u_e:

     An object of class *hclust* which describes the tree produced by
     the clustering process. The object is a list with components: 

   merge: an n-1 by 2 matrix. Row i of 'merge' describes the merging of
          clusters at step i of the clustering. If an element j in the
          row is negative, then observation -j was merged at this
          stage. If j is positive then the merge was with the cluster
          formed at the (earlier) stage j of the algorithm. Thus
          negative entries in 'merge' indicate agglomerations of
          singletons, and positive entries indicate agglomerations of
          non-singletons.

  height: a set of n-1 non-decreasing real values. The clustering
          _height_: that is, the value of the criterion associated with
          the clustering 'method' for the particular agglomeration.

   order: a vector giving the permutation of the original observations
          suitable for plotting, in the sense that a cluster plot using
          this ordering and matrix 'merge' will not have crossings of
          the branches.

  labels: labels for each of the objects being clustered.

    call: the call which produced the result.

  method: the cluster method that has been used.

dist.method: the distance that has been used to create 'd' (only
          returned if the distance object has a '"method"' attribute).

_A_u_t_h_o_r(_s):

     The 'hclustglad' function is based an Algorithm contributed to
     STATLIB by F. Murtagh.

_R_e_f_e_r_e_n_c_e_s:

     Everitt, B. (1974). _Cluster Analysis_. London: Heinemann Educ.
     Books.

     Hartigan, J. A. (1975). _Clustering  Algorithms_. New York: Wiley.

     Sneath, P. H. A. and R. R. Sokal (1973). _Numerical Taxonomy_. San
     Francisco: Freeman.

     Anderberg, M. R. (1973). _Cluster Analysis for Applications_.
     Academic Press: New York.

     Gordon, A. D. (1999). _Classification_. Second Edition. London:
     Chapman and Hall / CRC

     Murtagh, F. (1985). ``Multidimensional Clustering Algorithms'', in
     _COMPSTAT Lectures 4_. Wuerzburg: Physica-Verlag (for algorithmic
     details of algorithms used).

_S_e_e _A_l_s_o:

     'hclustglad' 'kmeans'.

_E_x_a_m_p_l_e_s:

     data(USArrests)
     hc <- hclustglad(dist(USArrests), "ave")
     plot(hc)
     plot(hc, hang = -1)

     ## Do the same with centroid clustering and squared Euclidean distance,
     ## cut the tree into ten clusters and reconstruct the upper part of the
     ## tree from the cluster centers.
     hc <- hclustglad(dist(USArrests)^2, "cen")
     memb <- cutree(hc, k = 10)
     cent <- NULL
     for(k in 1:10){
       cent <- rbind(cent, colMeans(USArrests[memb == k, , drop = FALSE]))
     }
     hc1 <- hclustglad(dist(cent)^2, method = "cen", members = table(memb))
     opar <- par(mfrow = c(1, 2))
     plot(hc,  labels = FALSE, hang = -1, main = "Original Tree")
     plot(hc1, labels = FALSE, hang = -1, main = "Re-start from 10 clusters")
     par(opar)

