Cluster analysis is a data exploration (mining) tool for dividing a multivariate dataset into “natural” clusters (groups). We use the methods to explore whether previously undefined clusters (groups) may exist in the dataset. For instance, a marketing department may wish to use survey results to sort its customers into categories (perhaps those likely to be most receptive to buying a product, those most likely to be against buying a product, and so forth).
Cluster Analysis is used when we believe that the sample units come from an unknown number of distinct populations or subpopulations. We also assume that the sample units come from a number of distinct populations, but there is no apriori definition of those populations. Our objective is to describe those populations using the observed data.
Cluster Analysis, until relatively recently, has had very little interest. This has changed because of the interest in the bioinformatics and genome research. To explore Cluster Analysis in our lesson here, we will use an ecological example.
Upon completion of this lesson, you should be able to do the following:
We will illustrate the various methods of cluster analysis using ecological data from Woodyard Hammock, a beechmagnolia forest in northern Florida. The data involve counts of the numbers of trees of each species in n = 72 sites. A total of 31 species were identified and counted, however, only p = 13 most common species were retained and are listed below. They are:
carcar  Carpinus caroliniana  Ironwood 
corflo  Cornus florida  Dogwood 
faggra  Fagus grandifolia  Beech 
ileopa  Ilex opaca  Holly 
liqsty  Liquidambar styraciflua  Sweetgum 
maggra  Magnolia grandiflora  Magnolia 
nyssyl  Nyssa sylvatica  Blackgum 
ostvir  Ostrya virginiana  Blue Beech 
oxyarb  Oxydendrum arboreum  Sourwood 
pingla  Pinus glabra  Spruce Pine 
quenig  Quercus nigra  Water Oak 
quemic  Quercus michauxii  Swamp Chestnut Oak 
symtin  Symplocus tinctoria  Horse Sugar 
The first column gives the 6letter code identifying the species, the second column gives its scientific name (Latin binomial), and the third column gives the common name for each species. The most commonly found of these species were the beech and magnolia.
We are to group sample sites together into clusters that share similar species compositions as determined by some measure of association. There are several options to measure association. Three common measures are listed below:
1. Measure of Association between Sample Units: We need some way to measure how similar two subjects or objects are to one another. This could be just about any type of measure of association. There is a lot of room for creativity here. However, SAS only allows Euclidean distance (defined later).
2. Measure of Association between Clusters  How similar are two clusters? There are dozens of techniques that can be used here .
Many different approaches to the cluster analysis problem have been proposed. The approaches generally fall into three broad categories:
1. Hierarchical methods:
Note 1: Agglomerative methods are used much more often than divisive methods.
Note 2: Hierarchical methods can be adapted to cluster variables rather than observations. This is a common use for hierarchical methods.
2. Nonhierarchical methods:
3. Model based methods:
A model based method uses a mixture model to specify the density function of the xvariables. In a mixture model, a population is modeled as a mixture of different subpopulations, each with the same general form for its probability density function (but each has different values for parameters such as the mean vector). For instance, the model may be a mixture of multivariate normal distributions. In cluster analysis, the algorithm provides a partition of the dataset that maximizes the likelihood function as defined by the mixture model. We won’t, however, cover this method any further in this course unit.
We use the standard notation that we have been using all along:
Johnson and Wichern list four different measures of association (similarity) that are frequently used with continuous variables in cluster analysis:
Euclidean Distance  This is used most commonly. For instance, in two dimensions, we can plot the observations in a scatter plot, and simply measure the distances between the pairs of points. More generally we can use the following equation:
\[d(\mathbf{X_i, X_j}) = \sqrt{\sum_{k=1}^{p}(X_{ik}  X_{jk})^2}\]
This is the squareroot of the sum of the squared differences between the measurements for each variable. (This is the only method that is available in SAS. In Minitab there are other distances like Pearson, Squared Euclidean etc)
Some other distances also use similar concept. For instance the Minkowski Distance is:
\[d(\mathbf{X_i, X_j}) = \left[\sum_{k=1}^{p}X_{ik}X_{jk}^m\right]^{1/m}\]
Here the square is replaced with raising the difference by a power of m and instead of taking the square root, we take the mth root.
Here are two other methods for measuring association:
Canberra Metric \[d(\mathbf{X_i, X_j}) = \sum_{k=1}^{p}\frac{X_{ik}X_{jk}}{X_{ik}+X_{jk}}\]
Czekanowski Coefficient \[d(\mathbf{X_i, X_j}) = 1 \frac{2\sum_{k=1}^{p}\text{min}(X_{ik},X_{jk})}{\sum_{k=1}^{p}(X_{ik}+X_{jk})}\]
For each of these distance measures, the smaller the distance, the more similar (more strongly associated) are the two subjects.
Or, if you like, you can invent your own measure! However, whatever you invent the measure of association must satisfy the following properties:
\(d(\mathbf{X_i, X_j}) = d(\mathbf{X_j, X_i})\)
i.e., the distance between subject one and subject two must be the same as the distance between subject two and subject one.
2. Positivity
\(d(\mathbf{X_i, X_j}) > 0\) if \(\mathbf{X_i} \ne \mathbf{X_j}\)
the distances must be positive  negative distances are not allowed!
3. Identity
\(d(\mathbf{X_i, X_j}) = 0\) if \(\mathbf{X_i} = \mathbf{X_j}\)
the distance between the subject and itself should be zero.
4. Triangle inequality
\(d(\mathbf{X_i, X_k}) \le d(\mathbf{X_i, X_j}) +d(\mathbf{X_j, X_k}) \)
This follows from geometric consideration, where we learnt that sum of two sides of a traingle cannot be smaller than the third side.
In the Woodyard Hammock example the observer has recorded how many individuals belonged to each species at each site. However, other research methods might find the observer recording whether or not the species was present at a site. In sociological studies we might be looking at traits which some people have but some others may not. Again, typically 1(0) signifies that the trait of interest is present (absent).
For sample units i and j, consider the following contingency table of frequencies of 11, 10, 01, and 00 matches across the variables:
Unit j


1

0

Total


Unit i 
1

a

b

a + b

0

c

d

c + d


Total

a + c

b + d

p = a + b + c + d

If we are comparing two subjects, subject i and subject j. a would be the number of variables which are present for both subjects. In the Woodyard Hammock example, this would be the number of species found at both sites. Again b would be the number (of species) found in subject i but not subject j ; c is just the opposite and d is the number that are not found in either subject.
From here we can calculate row totals, column totals and a grand total.
Johnson and Wichern list the following Similarity Coefficients that can be used for binary data:
The first coefficient looks at the number of matches (11 or 00) and divides by the total number of variables. If two sites had identical species lists, then this coefficient is equal to one since c = d = 0. The more species that are found at one and only one of the two sites, the smaller the value for this coefficient. If no species in one site are found in the opposite site, then this coefficient takes a value of zero, since in this case a = b = 0.
The remaining coefficients give different weights to matched (11 or 00) or mismatched (10 or 01) pairs. For, the second coefficient gives matched pairs double the weight, and thus emphasizes agreements in the species lists. In contrast, the third coefficient gives mismatched pairs double the weight, more strongly penalizing disagreements between the species lists. The remaining coefficients ignores species that are found in neither site.
The choice of coefficient will have an impact on the results of the analysis. Coefficients may be selected based on theoretical considerations specific to the problem at hand, or so as to yield the most parsimonious description of the data. For the latter, the analysis may be repeated using several of these coefficients. The coefficient that yields the most easily interpreted results is selected.
The main thing is that you need some measure of association between your subjects before the analysis can proceed .
We will look next at methods of measuring distances between clusters.
Combining Clusters in the Agglomerative Approach
In the agglomerative hierarchical approach, we start by defining each data point to be a cluster and combine existing clusters at each step. Here are four different methods for doing this:
1. Single Linkage: In single linkage, we define the distance between two clusters to be the minimum distance between any single data point in the first cluster and any single data point in the second cluster. On the basis of this definition of distance between clusters, at each stage of the process we combine the two clusters that have the smallest single linkage distance.
2. Complete Linkage: In complete linkage, we define the distance between two clusters to be the maximum distance between any single data point in the first cluster and any single data point in the second cluster. On the basis of this definition of distance between clusters, at each stage of the process we combine the two clusters that have the smallest complete linkage distance.
3. Average Linkage: In average linkage, we define the distance between two clusters to be the average distance between data points in the first cluster and data points in the second cluster. On the basis of this definition of distance between clusters, at each stage of the process we combine the two clusters that have the smallest average linkage distance.
4. Centroid Method: In centroid method, the distance between two clusters is the distance between the two mean vectors of the clusters. At each stage of the process we combine the two clusters that have the smallest centroid distance.
5. Ward’s Method: This method does not directly define a measure of distance between two points or clusters. It is an ANOVA based approach. At each stage, those two clusters marge, which provides the smallest increase in the combined error sum of squares from oneway univariate ANOVAs that can be done for each variable with groups defined by the clusters at that stage of the process
In the following table the mathematical form of the distances are provided. The graph gives geometric interpretation.
Note: None of these four methods is uniformly the best. In practice, it’s advisable to try several methods and then compare the results to form an overall judgment about the final formation of clusters.
Notationally, define
Single Linkage  \(d_{12} = \displaystyle \min_{i,j}\text{ } d(\mathbf{X}_i, \mathbf{Y}_j)\)  This is the distance between the closest members of the two clusters. 
Complete Linkage  \(d_{12} = \displaystyle \max_{i,j}\text{ } d(\mathbf{X}_i, \mathbf{Y}_j)\)  This is the distance between the members that are farthest apart (most dissimilar) 
Average Linkage  \(d_{12} = \frac{1}{kl}\sum_{i=1}^{k}\sum_{j=1}^{l}d(\mathbf{X}_i, \mathbf{Y}_j)\)  This method involves looking at the distances between all pairs and averages all of these distances. This is also called UPGMA  Unweighted Pair Group Mean Averaging. 
Centroid Method 
\( d_{12} = d(\bar{\mathbf{x}},\bar{\mathbf{y}})\) 
This involves finding the mean vector location for each of the clusters and taking the distance between these two centroids. 
Use your mouse to rollover the linkage method types listed on the right for a visual representation of how these distances are determined for each method.
SAS only uses Euclidian distance metric and agglomerative clustering, while Minitab can use Manhattan, Pearson, Squared Euclidean, and Square Pearson distances too. Both SAS and Minitab use only agglomerative clustering.
Cluster analysis is carried out in SAS using a cluster analysis procedure that is abbreviated as cluster. We will look at how this is carried out in the SAS program wood1.sas below.
Click on the arrow in the window below to see how to perform a cluster analysis using the Minitab statistical software application.
The results of cluster analysis are best summarized using a dendrogram. In a dendrogram, distance is plotted on one axis, while the sample units are given on the remaining axis. The tree shows how sample units are combined into clusters, the height of each branching point corresponding to the distance at which two clusters are joined.
In looking at the cluster history section of the SAS (or Minitab) output , we see that the Euclidean distance between sites 33 and 51 was smaller than between any other pair of sites (clusters). Therefore, this pair of sites was clustered first in the tree diagram. Following the clustering of these two sites, there are a total of n  1 = 71 clusters, and so, the cluster formed by sites 33 and 51 is designated "CL71" . Note that the numerical value of the distances in SAS and in Minitab are different. That is because SAS shows a 'normalized' distance. However we will not be interested in the absolute value of the distance. Their relative ranking is what we will use for cluster formation.
The Euclidean distance between sites 15 and 23 was smaller than between any other pair of the 70 heretofore unclustered sites or the distance between any of those sites and CL71. Therefore, this pair of sites was clustered second. Its designation is "CL70" .
In the seventh step of the algorithm, the distance between site 8 and cluster CL67 was smaller than the distance between any pair of heretofore unclustered sites and the distances between those sites and the existing clusters. Therefore, site 8 was joined to CL67 to form the cluster of 3 sites designated as CL65.
The clustering algorithm is completed when clusters CL2 and CL5 are joined.
The plot below is generated by Minitab. In SAS the diagram is horizontal. The color scheme depends on how many clusters we want (discussed later).
A decision regarding the optimum number of clusters is to be taken at some point. We also need to decide which clustering technique to be used. Therefore, we have adapted the wood1.sas program to specify use of the other clustering techniques. Here are links to these program changes. In Minitab also you may select other options instead of single linkage from the appropriate box.
wood1.sas  specifies complete linkage  
wood2.sas  is identical, except that it uses average linkage  
wood3.sas  uses the centroid method  
wood4.sas  uses the simple linkage 
As we run each of these programs we must remember to keep in mind that what we really after is a good description of the data.
First we want to compare results of the different clustering algorithms. Note that clusters containing one or only a few members are undesirable, as that will give rise to a large number of clusters, defeating the purpose of the whole analysis. That is not to say that, we can never have a cluster with a single member! In fact, if that happens, we need to investigate the reason. It may indicate that, the singleitem cluster is completely different from the other members of the sample and is best left alone.
To arrive at the optimum number of clusters we may follow this simple guideline. Select the number of clusters that have been identified by each method. This is accomplished by finding a break point (distance) below which further branching is ignored. In practice this is not necessarily straightforward. You will need to try a number different cut points to see which is more decisive. Here are the results of this type of partitioning using the different clustering algorithm methods on the Woodyard Hammock data. Dendrogram helps to determine the breakpoint.
For this example complete linkage yields the most satisfactory result.
For your convenience the following screenshots are provided to demonstrate how alternative clustering procedures may be done in Minitab.
The next step of the cluster analysis is to describe the clusters that we have identified.
Let us look at the SAS program below to see how this is implemented.
Notice that in the cluster procedure we created a new SAS dataset called clust1. This contains the information required by the tree procedure to draw the tree diagram.
In the tree procedure, we specified that 6 clusters will be investigated. A new SAS dataset called clust2 is created. This dataset will contain the id numbers of each site together with a new variable, called cluster, identifying which cluster that site belongs. What we need to do is merge this back with the original data so that we can describe the characteristics of each of the 6 clusters.
Now an Analysis of Variance for each species can be carried out specifying a class statement for the grouping variable, in this case, cluster.
We also include the means statement to get the cluster means.
Click on the arrow in the window below to get a walk through of how to perform a cluster analysis using the Minitab statistical software application.
We perform an analysis of variance for each of the tree species, comparing the means for those species across clusters. To control for experimentwise error rate, the Bonferroni method shall be applied. This means that we will reject the null hypothesis of equal means among clusters at level α if the pvalue is less than α/ p . Here, p = 13; so for an α = 0.05 level test, we reject the null hypothesis of equality of cluster means if the pvalue is less than 0.05/13 or 0.003846.
Here is the output for the species carcar.
We have collected the results of the individual species ANOVA's in the table below. The species names that are in boldface indicate significant results suggesting that there was significant variation among the clusters for that particular species. Note that the d.f. should always be presented.
Code

Species

F

pvalue

carcar

Ironwood

62.94

< 0.0001

corflo

Dogwood

1.55

0.1870

faggra

Beech

7.11

< 0.0001

ileopa

Holly

3.42

0.0082

liqsty

Sweetgum

5.87

0.0002

maggra

Magnolia

3.97

0.0033

nyssyl

Blackgum

1.66

0.1567

ostvir

Blue Beech

17.70

< 0.0001

oxyarb

Sourwood

1.42

0.2294

pingla

Spruce Pine

0.43

0.8244

quenig

Water Oak

2.23

0.0612

quemic

Swamp Chestnut Oak

4.12

0.0026

symtin

Horse Sugar

75.57

< 0.0001

d.f. = 5, 66
The results indicate that there are significant differences among clusters for ironwood, beech, sweetgum, magnolia, blue beech, swamp chestnut oak, and horse sugar.
Next, SAS computed the cluster means for each of the species. Here is a sample of the output with a couple of the significant species highlighted.
We have collected the cluster means for each of the significant species indicated above and placed these values in the table below:
Cluster


Code

1

2

3

4

5

6

carcar 
3.8

24.4

18.5

1.2

8.2

6.0

faggra 
11.4

6.4

5.9

5.9

8.6

2.7

liqsty 
7.2

17.4

6.4

6.8

6.6

18.0

maggra 
5.3

3.8

2.8

3.2

4.6

0.7

ostvir 
4.3

2.8

2.9

13.8

3.6

14.0

quemic 
5.3

5.2

9.4

4.1

7.0

2.3

symtin 
0.9

0.0

0.7

2.0

18.0

20.0

For each species, highlight the clusters where that species is abundant. For example, carcar (ironwood) is abundant in clusters 2 and 3. This operation is carried out across the rows of the table.
Each cluster is then characterized by the species that are highlighted in its column. For example, cluster 1 is characterized by a high abundance of faggra, or beech trees. This operation is carried out across the columns of the table.
In summary, we find:
It is also useful to summarize the results in the cluster diagram:
We can see that the two ironwood clusters (2 and 3) are joined. Ironwood is an understory species that tends to be found in wet regions that may be frequently flooded. Cluster 2 also contains sweetgum, an overstory species found in disturbed habitats, while cluster 3 contains swamp chestnut oak, an overstory species characteristic of undisturbed habitats.
Clusters 5 and 6 both contain horse sugar, an understory species characteristic of light gaps in the forest. Cluster 5 also contains beech and swamp chestnut oak, two overstory species characteristic of undisturbed habitats. These are likely to be saplings of the two species growing in the horse sugar light gaps. Cluster 6 also contains blue beech, an understory species similar to ironwood, but characteristic of uplands.
Cluster 4 is dominated by blue beech, an understory species characteristic of uplands
Cluster 1 is dominated by beech, an overstory species most abundant in undisturbed habitats.
From the above description, you can see that a meaningful interpretation of the results of a cluster analysis can best be obtained using subjectmatter knowledge.
This is an alternative approach for performing cluster analysis. Basically, it looks at cluster analysis as an analysis of variance problem, instead of using distance metrics or measures of association.
This method involves an agglomerative clustering algorithm. It will start out at the leaves and work its way to the trunk, so to speak. It looks for groups of leaves that form into branches, the branches into limbs and eventually into the trunk. Ward's method starts out with n clusters of size 1 and continues until all the observations are included into one cluster.
This method is most appropriate for quantitative variables, and not binary variables.
Based on the notion that clusters of multivariate observations should be approximately elliptical in shape, we assume that the data from each of the clusters have been realized in a multivariate distribution. Therefore, it would follow that they would fall into an elliptical shape when plotted in a pdimensional scatter plot.
Let X_{ijk} denote the value for variable k in observation j belonging to cluster i.
Furthermore, we define
We sum over all variables, and all of the units within each cluster. We compare individual observations for each variable against the cluster means for that variable. Note that when the Error Sum of Squares is small, it suggests that our data are close to their cluster means, implying that we have a cluster of like units.
The total sums of squares is defined in the same as always. Here we compare the individual observations for each variable against the grand mean for that variable.
This r^{2} value is interpreted as the proportion of variation explained by a particular clustering of the observations.
Using Ward's Method we start out with all sample units in n clusters of size 1 each. In the first step of the algorithm, n  1 clusters are formed, one of size two and the remaining of size 1. The error sum of squares and r^{2} values are then computed. The pair of sample units that yield the smallest error sum of squares, or equivalently, the largest r^{2} value will form the first cluster. Then, in the second step of the algorithm, n  2 clusters are formed from that n  1 clusters defined in step 2. These may include two clusters of size 2, or a single cluster of size 3 including the two items clustered in step 1. Again, the value of r^{2} is maximized. Thus, at each step of the algorithm clusters or observations are combined in such a way as to minimize the results of error from the squares or alternatively maximize the r^{2} value. The algorithm stops when all sample units are combined into a single large cluster of size n .
We will take a look at the implementation of Ward's Method using the SAS program wood5.sas. Minitab implementation is also similar. Minitab is not shown separately.
As you can see, this program is very similar to the previous program, (wood1.sas), that was discussed earlier in this lesson. The only difference is that we have specified that method=ward in the cluster procedure as highlighted above. The tree procedure is used to draw the tree diagram shown below, as well as to assign cluster identifications. Here we will look at four clusters.
We had decided earlier that we wanted four clusters.Ttherefore we put the break in the plot and have highlighted the resulting clusters. It looks as though there are two very well defined clusters because it shows a large break between the first and second branches of the tree. The partitioning results into 4 clusters yielding clusters of sizes 31, 24, 9, and 8.
Referring back to the SAS output, the results of the ANOVAs were found and have copied them here for discussion.
Results of ANOVA's


Code

Species

F

pvalue

carcar

Ironwood

67.42

< 0.0001

corflo

Dogwood

2.31

0.0837

faggra

Beech

7.13

0.0003

ileopa

Holly

5.38

0.0022

liqsty

Sweetgum

0.76

0.5188

maggra

Magnolia

2.75

0.0494

nyssyl

Blackgum

1.36

0.2627

ostvir

Blue Beech

32.91

< 0.0001

oxyarb

Sourwood

3.15

0.0304

pingla

Spruce Pine

1.03

0.3839

quenig

Water Oak

2.39

0.0759

quemic

Swamp Chestnut Oak

3.44

0.0216

symtin

Horse Sugar

120.95

< 0.0001

d.f. = 3, 68
We have boldfaced those species whose Fvalues, using a Bonferoni correction, show significance. These include Ironwood, Beech, Holly, Blue Beech and Horse Sugar.
Next we look at the cluster Means for these significant species:
Cluster


Code

1

2

3

4

carcar 
2.8

18.5

1.0

7.4

faggra 
10.6

6.0

5.9

6.4

ileopa 
7.5

4.3

12.3

7.9

ostvir 
5.4

3.1

18.3

7.5

symtin 
1.3

0.7

1.4

18.8

Again, we have boldfaced those values that show an abundance of that species within the different clusters.
Note that this interpretation is cleaner than the interpretation obtained earlier from the complete linkage method. This suggests that Ward's method may be preferred for the current data.
The results can then be summarized in the following dendrogram:
In summary, this method is performed in essentially the same manner as the previous method the only difference is that the cluster analysis is based on Analysis of Variance instead of distances.
This final method that we would like to examine is a nonhierarchical approach. This method was presented by MacQueen (1967) in the Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability.
One of the advantages of this method is that we do not have to calculate the distance measures between all pairs of subjects. Therefore, this procedure seems much more efficient or practical when you have very large datasets.
Under this procedure you need to prespecifiy how many clusters you want to consider. The clusters in this procedure do not form a tree. There are two approaches to carrying out the KMeans procedure. The approaches vary as to how the procedure begins the partitioning. The first approach is to do this randomly, to start out with a random partitioning of subjects into groups and go from there. The alternative is to start with an additional set of starting points. These would form the centers of our clusters. The random nature of the first approach will avoid bias.
Once this decision has been made, here is an overview of the process:
Step 1  Partition the items into K initial clusters.
Step 2  Scan through the list of n items, assigning each item to the cluster whose centroid (mean) is closest. Each time an item is reassigned we will recalculate the cluster mean or centroid for the cluster receiving that item and the cluster losing that item.
Step 3  Repeat Step 2 over and over again until no more reassignments are made.
Let's look at a simple example in order to see how this works. Here is an example where we have four items and only two variables:
Item

X_{1}

X_{2}

A 
7

9

B 
3

3

C 
4

1

D 
3

8

Suppose that items are initially decide to partition the items into two clusters (A, B) and (C, D). Then calculating the cluster centroids, or the mean of all the variables within the cluster, we would obtain:
For example , the mean of the first variable for cluster (A, B) is 5.
Next we calculate the distances between the item A and the centroids of clusters (A, B) and (C, D).
Here, we get a Euclidean distance between A and each of these cluster centroids. We see that item A is closer to cluster (A, B) than cluster (C, D). Therefore, we are going to leave item A in cluster (A, B)and no change is made at this point.
Next, we will look at the distance between item B and the centroids of clusters (A, B) and (C, D).
Here, we see that item B is closer to cluster (C, D) than cluster (A, B). Therefore, item B will be reassigned, resulting in the new clusters (A) and (B, C, D).
The centroids of the new clusters now changed are calculated as:
Next, we will calculate the distance between the items and each of the clusters (A) and (B, C, D).
It turns out that since all four items are closer to their current cluster centroids, no further reassignments are required.
We must note however, that the results of the Kmeans procedure can be sensitive to the initial assignment of clusters.
For example, suppose the items had initially been assigned to the clusters (A, C) and (B, D). Then the cluster centroids would be calculated as follows:
From here we can find that the distances between the items and the cluster centroids are:
Note that each item is closer to its cluster centroid than the opposite centroid. So, the initial cluster assignment is retained.
If this is the case, the which result should be used as our summary?
We can compute the sum of squared distances between the items and their cluster centroid. For our first clustering scheme for clusters (A) and (B, C, D), we had the following distances to cluster centroids:
So, the sum of squared distances is:
\(9.\bar{4} + 16.\bar{1} + 0 + 1.\bar{1} = 26.\bar{6}\)
For clusters (A, C) and (B, D), we had the following distances to cluster centroids:
So, the sum of squared distances is:
\(18.25 + 6.25 + 18.25 + 6.25 = 49. 0\)
We would conclude that since \(26.\bar{6} < 49.0\), this would suggest that the first clustering scheme is better and we would partition the items into the clusters (A) and (B, C, D).
In practice, several initial clusters should be tried and see which one gives you the best results. The question here arises, however, how should we define the initial clusters?
Now that you have a good idea of what is going to happen, we need to go back to our original question for this method... How should we define the initial clusters? Again, there are two main approaches that are taken to define these initial clusters.
The first approach is just to assign the clusters randomly. This does not seem like it would be a very efficient approach. The main reason to take this approach would be to avoid any bias in this process.
The second approach is to use a Leader Algorithm. (Hartigan, J.A., 1975, Clustering Algorithms). This involves the following procedure:
Step 1. Select the first item from the list. This item will form the centroid of the initial cluster.
Step 2. Search through the subsequent items until an item is found that is at least distance δ away from any previously defined cluster centroid. This item will form the centroid of the next cluster.
Step 3: Step 2 is repeated until all K cluster centroids are obtained, or no further items can be assigned.
Step 4: The initial clusters are obtained by assigning items to the nearest cluster centroids.
The following viewlet illustrates how this procedure for k = 4 clusters and p = 2 variables plotted in a scatter plot.
Now, let's take a look at each of these options in turn using our Woodyard Hammock dataset.
We first must determine:
In some applications, theory specific to the discipline may tell us how large K should be. In general, however, there is no prior knowledge that can be applied to find K. Our approach is to apply the following procedure for various values of K. For each K , we obtain a description of the resulting clusters. The value K is then selected to yield the most meaningful description. We wish to select K large enough so that the composition of the individual clusters is uniform, but not so large as to yield too complex a description for the resulting clusters.
Here, we shall take K = 4 and use the random assignment approach to find a reasonable value for δ.
This random approach is implemented in SAS using the following program titled wood6.sas.
The procedure that we will be using, shown above, is called fastclus, which stands for fast cluster analysis. This is designed specifically to develop results quickly especially with very large datasets. Remember, unlike the previous cluster analysis methods, we will not get a tree diagram out of this procedure.
First of all we need to specify the number of the clusters that we want to include. In this case we will ask for four. Then, we set replace=random, indicating the the initial cluster centroids will be randomly selected from the study subjects (sites).
Click on the arrow in the window below to see how to perform a cluster analysis using the Kmeans procedure in Minitab's statistical software.
Remember, when you run this program you will get different results because a different random set of subjects will be selected each time.
The first part of the output gives the initial cluster centers. SAS has picked four sites at random and lists how many species of each tree there are at each site.
The procedure then works iteratively until no reassignments can be obtained. The following table was copied from the SAS output for discussion purposes.
Cluster

Maximum Point to Centroid Distance

Nearest Cluster

Distance to Closest Cluster

1

21.1973

3

16.5910

2

20.2998

3

13.0501

3

22.1861

2

13.0501

4

23.1866

3

15.8186

In this case, we see that cluster 3 is the nearest neighboring cluster to cluster 1, and the distance between those two clusters is 16.591.
To set delta for the leader algorithm, however, we want to pay attention to maximum distances between the cluster centroids and the furthest apart site in that cluster. We can see that all of these maximum distances exceed 20. Therefore, based on these results, we will set the radius δ = 20.
Now, we can turn to wood7.sas where this radius δ value is used to run the Leader Algorithmic approach. Here is the SAS program modified to accommodate these changes:
The fastclus procedure is used again only this time with the leader algorithm options specified.
We set the maximum number of clusters to four and also set the radius to equal 20, the delta value that we found earlier.
Again, the output produces the initial cluster centroids. Given the first site, it will go down the list of the sites until it finds another site that is at least 20 away from this first point. The first one it finds forms the second cluster centroid. Then it goes down the list until it finds another site that is at least 20 away from the first two to form the third cluster centroid. Finally, the fourth cluster is formed by searching until it finds a site that is at least 20 away from the first three.
SAS also provides an iteration history showing what happens during each iterative of the algorithm. The algorithm stops after five iterations, showing the changes in the location of the centroids. In other words, convergence was achieved after 5 iterations.
Next, the SAS output provides a cluster summary which gives the number of sites in each cluster. It also tells you which cluster is closest. From this it seems that Cluster 1 is in the middle because three of the clusters (2,3,and 4) are closest to Cluster 1 and not the other clusters. What is reported are the distances between the cluster centroids and their nearest neighboring clusters. i.e., Cluster 1 is 14.3 away from Cluster 4. These results from all four clusters has been copied from the SAS out put and placed in the table below:
Cluster

Size

Nearest Neighbor

Distance

1

28

4

14.3126

2

9

1

17.6003

3

18

1

19.3971

4

17

1

14.3126

In comparing these spacings with the spacing that we found earlier, you will notice that these clusters are more widely spaced than the previously defined clusters.
The output of fastclus also gives the results of individuals ANOVAs for each species. However, only the r^{2} values for those ANOVAs are presented. The r^{2} values are computed, as usual, by dividing the model sum of squares by the total sum of squares. These are summarized in the following table:
Code

Species

r^{2}

r^{2}/(1  r^{2})

F

carcar

Ironwood

0.785

3.685

82.93

corflo

Dogwood

0.073

0.079

1.79

faggra

Beech

0.299

0.427

9.67

ileopa

Holly

0.367

0.579

13.14

liqsty

Sweetgum

0.110

0.123

2.80

maggra

Magnolia

0.199

0.249

5.64

nyssyl

Blackgum

0.124

0.142

3.21

ostvir

Blue Beech

0.581

1.387

31.44

oxyarb

Sourwood

0.110

0.124

2.81

pingla

Spruce Pine

0.033

0.034

0.76

quenig

Water Oak

0.119

0.135

3.07

quemic

Swamp Chestnut Oak

0.166

0.199

4.50

symtin

Horse Sugar

0.674

2.063

46.76

Given r^{2} , the Fstatistic can be obtained from the following formula:
\[F = \frac{r^2/(K1)}{(1r^2)/(nK)}\]
where K1 is the degrees of freedom between clusters and nK is the degrees of freedom within clusters.
In our example, n = 72 and K = 4. So, if we to take the ratio of r^{2} divided by 1r^{2} and multiply and multiply the result by 68, and divide by 3, we arrive at the Fvalues in the table.
Each of these Fvalues is going to be tested at K  1 = 3 and n  K = 68 degrees of freedom and using the Bonferoni correction, the critical value for an α = 0.05 level test is F _{3,68,0.05/13} = 4.90. Therefore, anything above 4.90 will be significant here. In this case the species in boldface in the table above are the species where where the Fvalue is above 4.90.
The next thing we want to do is to look at the cluster means for the significant species we identified above. Below we have listed these species along with the means for these species from the SAS output. As before, we have boldfaced the larger numbers within each row. As a result you can see that ironwood is most abundant in Cluster 3, Beech is most abundant in Cluster 1 and so forth...
Cluster


Species

1

2

3

4

Ironwood 
4.1

7.2

21.2

2.1

Beech 
11.1

6.1

5.7

6.2

Holly 
5.5

5.9

4.4

13.2

Magnolia 
5.3

3.3

2.8

3.0

Blue Beech 
4.5

5.3

2.4

14.6

Horse Sugar 
0.9

16.1

0.6

2.2

Now, in looking down the columns of the table we can characterize the individual clusters. We can see the following:
Cluster 1: Primarily Beech and Magnolia: There are the large canopy species typical of oldgrowth forest.
Cluster 2: Primarily Horse Sugar: These are a small understory species typical of smallscale disturbances (light gaps) in the forest.
Cluster 3: Primarily Ironwood: This is an understory species typical of wet habitats.
Cluster 4: Primarily Holly and Blue Beech: This is also an understory species typical of dry habitats.
In this lesson we learned about:
Complete the homework problems that will give you a chance to put what you have learned to use...