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CL CLA - CLAssification - Clusters

(8/22/97)

PURPOSE

To find clusters of images in factor space (or a selected subspace) by using Didat's method of moving centers, and to apply hierarchical ascendant classification (HAC) to the resulting cluster centers.

SEE ALSO

CL HC [CLassification - Hierarchical clustering]
CL HD [CLassification - Hierarchical clustering, calculate classes]
CL HE [CLassification - Hierarchical clustering, create doc files]
CL KM [CLassification - K Means clustering]

USAGE

.OPERATION: CL CLA

.INPUT FILE CODE: 6
[enter number of the IMC file previously created by CORAN85, which contains the image coordinates in factor space]

.CLUSTER FILE: CLU001
[enter name of cluster file where cluster and classification parameters are put for later reference]

.FACTORS TO BE USED: 1-3,6-8
[enter factors to be used in the cluster search]

.# OF ITER./PART., # OF CENTERS/PART.: 5,8
[enter number of iterations per partition, NIP (default=5) and number of centers, NS ("seeds") per partition (default=5)]

.# OF PARTITIONS: 3
[enter number of partitions, NP (default=4)]

.ENTER SEED INTEGER (0=RANDOM DRAW): 0
[If you wnat to precisely duplicate a previous run, you mutst enter the seed used in that run.]

.PERCENT FOR CLASS CUTOFF (0=NO CUTOFF): 2.4
[Only classes with equal or higher percentage of numbers are used in HAC.]

.DO YOU WANT DENDROGRAM PLOT FILE (Y/N): Y
[If the answer is "Y" a dendrogram plot metafile will be made.]

If the answer is "Y" the name of the plot metafile will be solicited:

.ENTER FILE NAME FOR DENDROGRAM: DEN001

.DO YOU WANT DENDROGRAM DOC FILE? (Y/N): Y
[Allows preparation of a document file containing the dendrogram tree.]

If the answer is "Y" the name of the document file will be solicited:

.DOCUMENT FILE: DOC000

.DO YOU WANT THE CLUSTER DOC FILE? (Y/N): Y
[Allows preparation of a cluster document file for

hierarchical clustering.]

If the answer is "Y" the name of the document file will be solicited:

.DOCUMENT FILE: DOC001

NOTES

  1. The RESULTS file contains the following information:
    A) Aggregation history:
    For each of the NP partitions, the NS seeds and sizes of clusters are listed. The non-empty clusters in the crossed partion are given by size and cummulative percentage. A total of NS**NP clusters are possible, but in practice, only 10% of these are non-empty.
    B) Description of hierarchy nodes in HAC:
    The nodes are numbered starting from the number of the highest cluster. For each node, the Senior, Junior, size, weight and hierarchy index are given. The hierarchy index is printed out as a histogram.
    C) Description of the hierarchy classes:
    For each node the constituent classes are listed.
    D) HAC Dendrogram:
    The class relationships are represented in the form of a dendrogram (tree structure). The lengths of the branches (in horizontal print direction) are proportional to the heirarchy indices.
    E) List of class members:
    The members are listed for each of the basic non-empty classes.
    F) List of class center coordinates:
    For each class, the NFAC coordinates of its center is listed. These are contained in the cluster file.
    G) Re-classification lookup table:
    Each cutoff point in the dendrogram, from right to left, defines a new classification scheme with the number of classes increasing by 1 each time. The table gives the new class memberships for any cutoff point selected.

  2. The algorithm underlying this set of programs is described by Lebart et al., MULTIVARIATE DESCRIPTIVE STATISTICAL ANALYSIS, J. Wiley and Sons, New York 1984, Chapter V, p.109.

  3. To display the plot use 'CS DI **' and enter the name of the plot metafile.

SUBROUTINES

SCLASSI, SCLASSY, SEMIS, NOYAU, CHAVA, DEUCL, RGRI, RCLU, DENDRO, ARBRE, PARST, STABK, SHELK, COUPE, CLAST, RETIR, SEN3A, CLUSTERLST

CALLER

UTIL1

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