Faster alternative to hclust
This function implements optimal hierarchical clustering with the same interface as
hclust.
hclust(d, method = "complete", members=NULL) flashClust(d, method = "complete", members=NULL)
d |
a dissimilarity structure as produced by 'dist'. |
method |
the agglomeration method to be used. This should be (an
unambiguous abbreviation of) one of |
members |
|
See the description of hclust for details on available clustering 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 hclust 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.
flashClust is a wrapper for compatibility with older code.
Returned value is the same as that of hclust:
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 |
height |
a set of n-1 non-decreasing real values.
The clustering height: that is, the value of
the criterion associated with the clustering
|
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 |
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 |
Fionn Murtagh, adapted and packaged by Peter Langfelder
This implementation is mentioned in
Peter Langfelder, Steve Horvath (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. http://www.jstatsoft.org/v46/i11/
F.Murtagh's software web site: http://www.classification-society.org/csna/mda-sw/ , section 6
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth \& Brooks/Cole. (S version.)
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).
McQuitty, L.L. (1966). Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825–831.
# generate some data to cluster
set.seed(1);
nNodes = 2000;
# Random "distance" matrix
dst = matrix(runif(n = nNodes^2, min = 0, max = 1), nNodes, nNodes);
# Time the flashClust clustering
system.time( {
h1 = hclust(as.dist(dst), method= "average");
} );
# Time the standard R clustering
system.time( {
h2 = stats::hclust(as.dist(dst), method = "average");
} );
all.equal(h1, h2)
# What is different:
h1[[6]]
h2[[6]]
# Everything but the 'call' component is the same; in particular, the trees are exactly equal.Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.