# Hierarchical Clustering Hierarchical cluster analysis. ```r hcluster(x, method = "euclidean", diag = FALSE, upper = FALSE, link = "complete", members = NULL, nbproc = 2, doubleprecision = TRUE) ``` ## Arguments - `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). Or an object of class "exprSet". - `method`: the distance measure to be used. This must be one of `"euclidean"`, `"maximum"`, `"manhattan"`, `"canberra"`, `"binary"`, `"pearson"`, `"abspearson"`, `"correlation"`, `"abscorrelation"`, `"spearman"` or `"kendall"`. Any unambiguous substring can be given. - `diag`: logical value indicating whether the diagonal of the distance matrix should be printed by `print.dist`. - `upper`: logical value indicating whether the upper triangle of the distance matrix should be printed by `print.dist`. - `link`: the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of `"ward"`, `"single"`, `"complete"`, `"average"`, `"mcquitty"`, `"median"` or `"centroid"`,`"centroid2"`, `"ward.d2"`. - `members`: `NULL` or a vector with length size of `d`. - `nbproc`: integer, number of subprocess for parallelization [Linux & Mac only] - `doubleprecision`: True: use of double precision for distance matrix computation; False: use simple precision ## Returns 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). There is a `print` and a `plot` method for `hclust` objects. The `plclust()` function is basically the same as the plot method, `plot.hclust`, primarily for back compatibility with S-plus. Its extra arguments are not yet implemented. ## Details This function is a mix of function `hclust` and function `dist`. `hcluster(x, method = "euclidean",link = "complete") = hclust(dist(x,method = "euclidean"),method = "complete"))` It use twice less memory, as it doesn't store distance matrix. For more details, see documentation of `hclust` and `Dist`. ## Note Multi-thread (parallelisation) is disable on Windows. ## Author(s) The `hcluster` function is based on C code adapted from Cran Fortran routine by Antoine Lucas. ## See Also `Dist`, `hclust`, `kmeans`. ## References Antoine Lucas and Sylvain Jasson, **Using amap and ctc Packages for Huge Clustering**, R News, 2006, vol 6, issue 5 pages 58-60. ## Examples ```r data(USArrests) hc <- hcluster(USArrests,link = "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 <- hclust(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 <- hclust(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) ## other combinaison are possible hc <- hcluster(USArrests,method = "euc",link = "ward", nbproc= 1, doubleprecision = TRUE) hc <- hcluster(USArrests,method = "max",link = "single", nbproc= 2, doubleprecision = TRUE) hc <- hcluster(USArrests,method = "man",link = "complete", nbproc= 1, doubleprecision = TRUE) hc <- hcluster(USArrests,method = "can",link = "average", nbproc= 2, doubleprecision = TRUE) hc <- hcluster(USArrests,method = "bin",link = "mcquitty", nbproc= 1, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "pea",link = "median", nbproc= 2, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "abspea",link = "median", nbproc= 2, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "cor",link = "centroid", nbproc= 1, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "abscor",link = "centroid", nbproc= 1, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "spe",link = "complete", nbproc= 2, doubleprecision = FALSE) hc <- hcluster(USArrests,method = "ken",link = "complete", nbproc= 2, doubleprecision = FALSE) ```

hcluster function