perform hierarchical clustering on a difference matrix
- Single Link (Nearest Neighbor), also: -n
- Complete Link (Furthest Neighbor)
- Group Average (UPGMA: Unweighted Pair Group Method using Arithmetic averages)
- Weighted Average (WPGMA: Weighted Pair Group Method using Arithmetic averages)
- Unweighted Centroid (Centroid, UPGMC: Unweighted Pair Group Method using Centroids)
- Weighted Centroid (Median, WPGMC: Weighted Pair Group Method using Centroids)
- Ward's Method (Minimum Variance), also: -w
- Binary difference output, instead of a cluster file. The result is a difference matrix file.
- Cophenetic difference output, instead of cluster file. The result is a difference matrix file.
- -m int
- -m int-int
- -m int-int+int
- Maximum number of clusters for binary or cophenetic output. This is the maximum for
each run (option -r), so there may very well appear more clusters
than given by this number.
You can define ranges of numbers, for instance:
The first example selects all values from 2 to 8 inclusive, the second selects
the values 2 5 8 11.
- -N float
- Noise. Before clustering, all values are increased by a random value
between zero and sd times the specified value, where sd
is the standard deviation of all the original values. This option can be used
more than once, if -b or -c are used as well.
- -o filename
- Output file
- -r int
- Number of runs. Only useful if -b or -c, and -N are used as well.
- -s int
- Seed for random number generator.
This program performs hierarchical clustering on a
difference matrix file
and produces a
hierarchical cluster definition file
option -b or -c was used.
This clustering file can then be processed further with
to create an image of a dendrogram, or
to produce a partitioning of the
data. With option -b or -c, the result can be used to create a differentiated
cluster map with mapdiff
The program will abort if a file _CANCEL_.L04 exists in the current directory,
or if it is created while the program is running. This is useful for stopping
long running calculations from a GUI, such as pyL04
The clustering algorithms implemented in this program are described in:
Anil K. Jain and Richard C. Dubes.
Algorithms for Clustering Data.
Prentice Hall, Englewood Cliffs, NJ, 1988.