::Free Statistics and Forecasting Software::

All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.

This free online software (calculator) computes the agglomerative nesting (hierarchical clustering) of a multivariate dataset as proposed by Kaufman and Rousseeuw. At each level the two nearest clusters are merged to form the next cluster.
This procedure computes the 'agglomerative coefficient' which can be interpreted as the amount of clustering structure that has been found. In addition, this procedure generates the so-called banner display.
The following clustering methods are currently supported: unweighted pair-group average, single linkage, complete linkage, Ward's method, weighted average linkage, and the 'flexible' method based on the Lance-Williams formula.

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

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Source code of R module

par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) par6 <- as.numeric(par6) par7 <- as.numeric(par7) library(cluster) if (par2 == "flexible") { if (par3 == 1) pm <- c(par4) if (par3 == 3) pm <- c(par4,par5,par6) if (par3 == 4) pm <- c(par4,par5,par6,par7) ag <- agnes(t(y),metric=par1,method=par2,par.method=pm) } else { ag <- agnes(t(y),metric=par1,method=par2) } mysub <- paste("Method: ",par2) summary(ag) bitmap(file="test1.png") plot(ag,which.plots=2,main=main,sub=mysub,xlab=xlab,ylab=ylab) dev.off() bitmap(file="test2.png") plot(ag,which.plots=1,main="Banner",sub=mysub,xlab=ylab,ylab=xlab) dev.off() load(file="createtable") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Agglomerative Nesting (Hierarchical Clustering)",2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,"Agglomerative Coefficient",header=TRUE) a<-table.element(a,ag$ac) a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable.tab")

Cite this software as:

Wessa, P., (2017), Agglomerative Nesting (v1.0.5) in Free Statistics Software (v1.2.1), Office for Research Development and Education, URL https://www.wessa.net/rwasp_agglomerativehierarchicalclustering.wasp/

The R code is based on :

Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.

Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996): Clustering in an Object-Oriented Environment. Journal of Statistical Software, nr 1. URL: http://www.stat.ucla.edu/journals/jss/

Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, nr 26, 17-37

Lance, G.N., and W.T. Williams (1966). A General Theory of Classifactory Sorting Strategies, I. Hierarchical Systems, Computer J., nr 9, 373-380

To cite Wessa.net in publications use:
Wessa, P. (2024), Free Statistics Software, Office for Research Development and Education,
version 1.2.1, URL https://www.wessa.net/

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Software Version : 1.2.1
Algorithms & Software : Patrick Wessa, PhD
Server : wessa.net

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