实现用于聚类的邻近矩阵

Question

如果这个问题听起来微不足道或基本的话,请原谅我.

我有一组数据集(一堆字是特定的),我需要通过使用彼此的编辑距离来生成邻近矩阵,以找到并生成邻近矩阵.

然而,我很困惑如何跟踪矩阵中的数据/字符串.我需要接近矩阵用于聚类.

或者你在这个领域如何处理这类问题.我使用perl和R来实现这一点.

这是我编写的perl中的典型代码,它从包含我的文字包的文本文件中读取

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.  $!");

}

问题是如何从中表示邻近矩阵,并且仍然能够跟踪哪个比较表示我的矩阵中的哪个.

Answer 1

在RecordLinkage包中有levenshteinDist功能,这是计算字符串之间的编辑距离的一种方式.

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

设置一些数据:

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

现在创建一个由零组成的矩阵,为距离表保留内存.然后使用嵌套for循环计算各个距离.我们以每个水果的行和列的矩阵结束.因此,我们可以将列和行重命名为与原始向量相同.

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

结果:

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