CODEWITHSUNDEEP
rmachine-learningcluster-analysisdata-mining
damola
damola

Reputation: 292

Implementing proximity matrix for clustering

Please I am a little new to this field so pardon me if the question sound trivial or basic.

I have a group of dataset(Bag of words to be specific) and I need to generate a proximity matrix by using their edit distance from each other to find and generate the proximity matrix .

I am however quite confused how I will keep track of my data/strings in the matrix. I need the proximity matrix for the purpose of clustering.

Or How generally do you approach this kinds of problem in the field. I am using perl and R to implement this.

Here is a typical code in perl I have written that reads from a text file containing my bag of words

use strict ;
   use warnings ; 
   use Text::Levenshtein qw(distance) ;
   main(@ARGV);
   sub main
   {    
    my @TokenDistances ;
    my $Tokenfile  = 'TokenDistinct.txt';
    my @Token ;
    my $AppendingCount  = 0 ; 
    my @Tokencompare ;  
    my %Levcount  = ();
    open (FH ,"< $Tokenfile" ) or die ("Error opening file . $!");
     while(<FH>)
     {
        chomp $_;
        $_ =~ s/^(\s+)$//g;
        push (@Token , $_ ); 
     }
    close(FH); 
     @Tokencompare = @Token ; 


     foreach my $tokenWord(@Tokencompare)
     { 
        my $lengthoffile =  scalar @Tokencompare;
        my $i = 0 ;
        chomp $tokenWord ;

        #@TokenDistances = levDistance($tokenWord , \@Tokencompare );
        for($i = 0 ; $i < $lengthoffile ;$i++)
        {
            if(scalar @TokenDistances ==  scalar @Tokencompare)
            {
                print "Yipeeeeeeeeeeeeeeeeeeeee\n";
            }
            chomp $tokenWord   ;
            chomp $Tokencompare[$i];
            #print   $tokenWord. "   {$Tokencompare[$i]}  " . "      $TokenDistances[$i] " . "\n";
            #$Levcount{$tokenWord}{$Tokencompare[$i]} = $TokenDistances[$i];
            $Levcount{$tokenWord}{$Tokencompare[$i]} = levDistance($tokenWord , $Tokencompare[$i] );

        }

        StoreSortedValues ( \%Levcount ,\$tokenWord , \$AppendingCount);
        $AppendingCount++;
        %Levcount = () ;

     } 
    # %Levcount  = (); 
}

sub levDistance
{
    my $string1 = shift ;
    #my @StringList = @{(shift)};
    my $string2 =  shift ;
    return distance($string1 , $string2);
}


sub StoreSortedValues {


    my $Levcount  = shift;
    my $tokenWordTopMost = ${(shift)} ; 
    my $j = ${(shift)};
    my @ListToken;
    my $Tokenfile = 'LevResult.txt';

    if($j == 0 )
    {
        open (FH ,"> $Tokenfile" ) or die ("Error opening file . $!");
    }
    else
    {
        open (FH ,">> $Tokenfile" ) or die ("Error opening file . $!");
    }

                print $tokenWordTopMost; 
                my %tokenWordMaster = %{$Levcount->{$tokenWordTopMost}};
                @ListToken = sort { $tokenWordMaster{$a} cmp $tokenWordMaster{$b} }   keys %tokenWordMaster;
            #@ListToken = keys %tokenWordMaster;

        print FH "-------------------------- " . $tokenWordTopMost . "-------------------------------------\n";
        #print FH  map {"$_  \t=>  $tokenWordMaster{$_} \n "}   @ListToken;
        foreach my $tokey (@ListToken)
        {
            print FH  "$tokey=>\t" . $tokenWordMaster{$tokey} . "\n" 

        }

        close(FH) or  die ("Error Closing File.  $!");

}

the problem is how can I represent the proximity matrix from this and still be able to keep track of which comparison represent which in my matrix.

Upvotes: 6

Views: 2412

Answers (2)

Andrie
Andrie

Reputation: 179578

In the RecordLinkage package there is the levenshteinDist function, which is one way of calculating an edit distance between strings.

install.packages("RecordLinkage")
library(RecordLinkage)

Set up some data:

fruit <- c("Apple", "Apricot", "Avocado", "Banana", "Bilberry", "Blackberry", 
    "Blackcurrant", "Blueberry", "Currant", "Cherry")

Now create a matrix consisting of zeros to reserve memory for the distance table. Then use nested for loops to calculate the individual distances. We end with a matrix with a row and a column for each fruit. Thus we can rename the columns and rows to be identical to the original vector.

fdist <- matrix(rep(0, length(fruit)^2), ncol=length(fruit))
for(i in seq_along(fruit)){
  for(j in seq_along(fruit)){
    fdist[i, j] <- levenshteinDist(fruit[i], fruit[j])
  }
}
rownames(fdist) <- colnames(fdist) <- fruit

The results:

fdist

             Apple Apricot Avocado Banana Bilberry Blackberry Blackcurrant
Apple            0       5       6      6        7          9           12
Apricot          5       0       6      7        8         10           10
Avocado          6       6       0      6        8          9           10
Banana           6       7       6      0        7          8            8
Bilberry         7       8       8      7        0          4            9
Blackberry       9      10       9      8        4          0            5
Blackcurrant    12      10      10      8        9          5            0
Blueberry        8       9       9      8        3          3            8
Currant          7       5       6      5        8         10            6
Cherry           6       7       7      6        4          6           10

Upvotes: 7

Iterator
Iterator

Reputation: 20570

The proximity or similarity (or dissimilarity) matrix is just a table that stores the similarity score for pairs of objects. So, if you have N objects, then the R code can be simMat <- matrix(nrow = N, ncol = N), and then each entry, (i,j), of simMat indicates the similarity between item i and item j.

In R, you can use several packages, including vwr, to calculate the Levenshtein edit distance.

You may also find this Wikibook to be of interest: http://en.wikibooks.org/wiki/R_Programming/Text_Processing

Upvotes: 2

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