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Find the ideal cluster

4 messages · Jovani T. de Souza, David L Carlson, Michael Dewey

Original
Jovani T. de Souza
# 
So, I and some other colleagues developed a hierarchical clustering
algorithm to basically find the main clusters involving agricultural
industries according to a particular city (e.g. London city).. We
structured this algorithm in R. It is working perfectly. So, according to
our filters that we inserted in the algorithm, we were able to generate 6
clustering scenarios to London city. For example, the first scenario
generated 2 clusters, the second scenario 5 clusters, and so on. I would
therefore like some help on how I can choose the most appropriate one. I
saw that there are some packages that help in this process, like `pvclust`,
but I couldn't use it for my case. I am inserting a brief executable code
below to show the essence of what I want.

Any help is welcome! If you know how to use using another package, feel
free to describe.

Best Regards.


    library(rdist)
    library(geosphere)
    library(fpc)


    df<-structure(list(Industries = c(1,2,3,4,5,6),
    +                    Latitude = c(-23.8, -23.8, -23.9, -23.7,
-23.7,-23.7),
    +                    Longitude = c(-49.5, -49.6, -49.7, -49.8,
-49.6,-49.9),
    +                    Waste = c(526, 350, 526, 469, 534, 346)), class =
"data.frame", row.names = c(NA, -6L))

    df1<-df

    #clusters
    coordinates<-df[c("Latitude","Longitude")]
    d<-as.dist(distm(coordinates[,2:1]))
    fit.average<-hclust(d,method="average")

    clusters<-cutree(fit.average, k=2)
    df$cluster <- clusters
    > df
      Industries Latitude Longitude Waste cluster
    1          1    -23.8     -49.5   526       1
    2          2    -23.8     -49.6   350       1
    3          3    -23.9     -49.7   526       1
    4          4    -23.7     -49.8   469       2
    5          5    -23.7     -49.6   534       1
    6          6    -23.7     -49.9   346       2
    >
    clusters1<-cutree(fit.average, k=5)
    df1$cluster <- clusters1
    > df1
      Industries Latitude Longitude Waste cluster
    1          1    -23.8     -49.5   526       1
    2          2    -23.8     -49.6   350       1
    3          3    -23.9     -49.7   526       2
    4          4    -23.7     -49.8   469       3
    5          5    -23.7     -49.6   534       4
    6          6    -23.7     -49.9   346       5
    >
David L Carlson
# 
Look at the Cluster Analysis Task View, particularly section
"Additional Functionality"
(https://cran.r-project.org/web/views/Cluster.html)

Maybe package clValid:

The R package clValid contains functions for validating the results
of a clustering analysis. There are three main types of cluster
validation measures available, ?internal?, ?stability?, and
?biological?. The user can choose from nine clustering algorithms in
existing R packages, including hierarchical, K-means, self-organizing
maps (SOM), and model based clustering. In addition, we provide a
function to perform the self-organizing tree algorithm (SOTA) method
of clustering. Any combination of validation measures and clustering
methods can be requested in a single function call. This allows the
user to simultaneously evaluate several clustering algorithms while
varying the number of clusters, to help determine the most appropriate
method and number of clusters for the dataset of interest.
Additionally, the package can automatically make use of the biological
information contained in the Gene Ontology (GO) database to calculate
the biological validation measures, via the annotation packages
available in Bioconductor. The function returns an object of S4 class
clValid, which has summary, plot, print, and additional methods which
allow the user to display the optimal validation scores and extract
clustering results.

David L Carlson
Professor Emeritus
Texas A&M University


On Sat, Dec 12, 2020 at 9:27 AM Jovani T. de Souza
<jovanisouza5 at gmail.com> wrote:
Michael Dewey
# 
Dear Jovani

If you cross-post on CrossValidated as well as here it is polite to give 
a link so people do not answer here when someone has already answered 
there, or vice versa.

Michael
On 12/12/2020 15:27, Jovani T. de Souza wrote:

  
    
  


  


      

    

    
2 days later

    

      
      

        


  

        

      Jovani T. de Souza
    

    

      
      #
    

  


  

      
Thank you so much!

Sorry Michael, I will insert in the next.

Best regards.




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14/12/20
19:11:49

Em s?b., 12 de dez. de 2020 ?s 14:06, Michael Dewey <lists at dewey.myzen.co.uk>
escreveu: