To illustrate the effect of standardization in cluster analysis, this example uses the Fish
data set described in the "Getting Started" section of Chapter 38: The FASTCLUS Procedure. The numbers are measurements taken on 159 fish caught from the same lake (Laengelmaevesi) near Tampere in Finland (Puranen,
1917). The fish data set is available from the Sashelp
library.
The species (bream, parkki, pike, perch, roach, smelt, and whitefish), weight, three different length measurements (measured from the nose of the fish to the beginning of its tail, the notch of its tail, and the end of its tail), height, and width of each fish are recorded.
A couple of new variables are created in the Fish
data set: Weight3
and logLengthRatio
. The weight of a fish indicates its size—a heavier pike tends to be larger than a lighter pike. To get a one-dimensional
measure of the size of a fish, take the cubic root of the weight (Weight3
). The variables Height
, Width
, Length1
, Length2
, and Length3
are rescaled in order to adjust for dimensionality. The logLengthRatio
variable measures the tail length.
Because the new variables Weight3
–logLengthRatio
depend on the variable Weight
, observations with missing values for Weight
are not added to the data set. Consequently, there are 157 observations in the SAS data set Sashelp.Fish
.
Before you perform a cluster analysis on coordinate data, it is necessary to consider scaling or transforming the variables since variables with large variances tend to have a larger effect on the resulting clusters than variables with small variances do.
This example uses three different approaches to standardize or transform the data prior to the cluster analysis. The first approach uses several standardization methods provided in the STDIZE procedure. However, since standardization is not always appropriate prior to the clustering (see Milligan and Cooper (1987) for a Monte Carlo study on various methods of variable standardization), the second approach performs the cluster analysis with no standardization. The third approach invokes the ACECLUS procedure to transform the data into a within-cluster covariance matrix.
The clustering is performed by the FASTCLUS procedure to find seven clusters. Note that the variables Length2
and Length3
are eliminated from this analysis since they both are significantly and highly correlated with the variable Length1
. The correlation coefficients are 0.9958 and 0.9604, respectively. An output data set is created, and the FREQ procedure
is invoked to compare the clusters with the species classification.
The DATA step is as follows:
title 'Fish Measurement Data'; data Fish; set sashelp.fish; if Weight <= 0 or Weight = . then delete; Weight3 = Weight ** (1/3); Height = Height / Weight3; Width = Width / Weight3; Length1 = Length1 / Weight3; Length2 = Length2 / Weight3; Length3 = Length3 / Weight3; LogLengthRatio = log(Length3 / Length1); run;
The following macro, Std
, standardizes the Fish
data. The macro reads a single argument, mtd
, which selects the METHOD= specification to be used in PROC STDIZE.
/*--- macro for standardization ---*/ %macro Std(mtd); title2 "Data are Standardized by PROC STDIZE with METHOD= &mtd"; proc stdize data=fish out=sdzout method=&mtd; var Length1 logLengthRatio Height Width Weight3; run; %mend Std;
The following macro, FastFreq
, includes a PROC FASTCLUS statement for performing cluster analysis and a PROC FREQ statement for crosstabulating species
with the cluster membership information that is derived from the previous PROC FASTCLUS statement. The macro reads a single
argument, ds
, which selects the input data set to be used in PROC FASTCLUS.
/*--- macro for clustering and crosstabulating ---*/ /*--- cluster membership with species ---*/ %macro FastFreq(ds); proc fastclus data=&ds out=clust maxclusters=7 maxiter=100 noprint; var Length1 logLengthRatio Height Width Weight3; run; proc freq data=clust; tables species*cluster; run; %mend FastFreq;
The following analysis (labeled 'Approach 1') includes 18 different methods of standardization followed by clustering. Since there is a large amount of output from this approach, only results from METHOD=STD, METHOD=RANGE, METHOD=AGK(0.14), and METHOD=SPACING(0.14) are shown. The following statements produce Output 94.1.1 through Output 94.1.4.
/* Approach 1: data are standardized by PROC STDIZE */ %Std(MEAN); %FastFreq(sdzout); %Std(MEDIAN); %FastFreq(sdzout); %Std(SUM); %FastFreq(sdzout); %Std(EUCLEN); %FastFreq(sdzout); %Std(USTD); %FastFreq(sdzout); %Std(STD); %FastFreq(sdzout); %Std(RANGE); %FastFreq(sdzout); %Std(MIDRANGE); %FastFreq(sdzout); %Std(MAXABS); %FastFreq(sdzout); %Std(IQR); %FastFreq(sdzout); %Std(MAD); %FastFreq(sdzout); %Std(AGK(.14)); %FastFreq(sdzout); %Std(SPACING(.14)); %FastFreq(sdzout); %Std(ABW(5)); %FastFreq(sdzout); %Std(AWAVE(5)); %FastFreq(sdzout); %Std(L(1)); %FastFreq(sdzout); %Std(L(1.5)); %FastFreq(sdzout); %Std(L(2)); %FastFreq(sdzout);
Output 94.1.1: Data Are Standardized by PROC STDIZE with METHOD=STD
Fish Measurement Data |
Data are Standardized by PROC STDIZE with METHOD= STD |
|
|
Output 94.1.2: Data Are Standardized by PROC STDIZE with METHOD=RANGE
Fish Measurement Data |
Data are Standardized by PROC STDIZE with METHOD= RANGE |
|
|
Output 94.1.3: Data Are Standardized by PROC STDIZE with METHOD=AGK(0.14)
Fish Measurement Data |
Data are Standardized by PROC STDIZE with METHOD= AGK(.14) |
|
|
Output 94.1.4: Data Are Standardized by PROC STDIZE with METHOD=SPACING(0.14)
Fish Measurement Data |
Data are Standardized by PROC STDIZE with METHOD= SPACING(.14) |
|
|
The following analysis (labeled 'Approach 2') applies the cluster analysis directly to the original data. The following statements produce Output 94.1.5.
/* Approach 2: data are untransformed */ title2 'Data are Untransformed'; %FastFreq(fish);
Output 94.1.5: Untransformed Data
Fish Measurement Data |
Data are Untransformed |
|
|
The following analysis (labeled 'Approach 3') transforms the original data with the ACECLUS procedure and creates a TYPE=ACE output data set that is used as an input data set for the cluster analysis. The following statements produce Output 94.1.6.
/* Approach 3: data are transformed by PROC ACECLUS */ title2 'Data are Transformed by PROC ACECLUS'; proc aceclus data=fish out=ace p=.02 noprint; var Length1 logLengthRatio Height Width Weight3; run; %FastFreq(ace);
Output 94.1.6: Data Are Transformed by PROC ACECLUS
Fish Measurement Data |
Data are Transformed by PROC ACECLUS |
|
|
Table 94.4 displays a table summarizing each classification results. In this table, the first column represents the standardization
method, the second column represents the number of clusters that the seven species are classified into, and the third column
represents the total number of observations that are misclassified.
Table 94.4: Summary of Clustering Results
Method of Standardization |
Number of Clusters |
Misclassification |
---|---|---|
MEAN |
5 |
71 |
MEDIAN |
5 |
71 |
SUM |
6 |
51 |
EUCLEN |
6 |
45 |
USTD |
6 |
45 |
STD |
5 |
33 |
RANGE |
7 |
32 |
MIDRANGE |
7 |
32 |
MAXABS |
7 |
26 |
IQR |
5 |
28 |
MAD |
4 |
35 |
ABW(5) |
6 |
34 |
AWAVE(5) |
6 |
29 |
AGK(0.14) |
7 |
28 |
SPACING(0.14) |
7 |
25 |
L(1) |
6 |
41 |
L(1.5) |
5 |
33 |
L(2) |
5 |
33 |
untransformed |
5 |
71 |
PROC ACECLUS |
5 |
71 |
Consider the results displayed in Output 94.1.1. In that analysis, the method of standardization is STD, and the number of clusters and the number of misclassifications are computed as shown in Table 94.5.
Table 94.5: Computations of Numbers of Clusters and Misclassification When Standardization Method Is STD
Species |
Cluster Number |
Misclassification in Each Species |
---|---|---|
Bream |
6 |
0 |
Parkki |
5 |
0 |
Perch |
7 |
29 |
Pike |
1 |
0 |
Roach |
7 |
0 |
Smelt |
3 |
1 |
Whitefish |
7 |
3 |
In Output 94.1.1, the bream species is classified as cluster 6 since all 34 bream are categorized into cluster 6 with no misclassification. A similar pattern is seen with the roach, parkki, pike, and smelt species.
For the whitefish species, two fish are categorized into cluster 2, one fish is categorized into cluster 4, and three fish are categorized into cluster 7. Because the majority of this species is categorized into cluster 7, it is recorded in Table 94.5 as being classified as cluster 7 with 3 misclassifications. A similar pattern is seen with the perch species: it is classified as cluster 7 with 29 misclassifications.
In summary, when the standardization method is STD, seven species of fish are classified into only five clusters and the total number of misclassified observations is 33.
The result of this analysis demonstrates that when variables are standardized by the STDIZE procedure with methods including
RANGE, MIDRANGE, MAXABS, AGK(0.14), and SPACING(0.14), the FASTCLUS procedure produces the correct number of clusters and
less misclassification than it does when other standardization methods are used. The SPACING method attains the best result,
probably because the variables Length1
and Height
both exhibit marked groupings (bimodality) in their distributions.