The Cluster Centroids Report provides a summary of the patterns that are identified by the clustering procedure. This report allows you to determine the characteristics of the patterns that are identified. Figure 11-2 illustrates a sample Cluster Centroids Report.
CLUSTER CENTROIDS CLUSTER JOBMXNTA JOBTCBTM JOBNLR RADIUS NO ASSIGN PCT ------- -------- -------- -------- ------ --------- --- 1 0.0 0:00:05 3450.7 2.00 145 7 2 1.0 0:00:03 277.8 1.00 163 8 3 0.0 0:00:57 90.9 3.00 68 3 4 1.0 0:00:23 64.4 2.50 56 3 5 0.0 0:00:14 181.8 1.00 156 8 6 2.0 0:00:03 209.3 3.00 58 3 7 0.0 0:00:01 267.7 1.00 1172 59 8 0.0 0:00:07 8165.7 3.00 54 3 9 1.0 0:00:03 3950.7 2.50 20 1 10 0.0 0:00:11 12211.6 3.00 81 4 11 0.0 0:00:36 261.4 2.00 27 1
Figure 11-2. Cluster Assignment - Randomly Selected Sample Report
The Cluster Centroids Report lists the following fields:
CLUSTER: The cluster number. Cluster numbers are
assigned sequentially to the patterns that are
identified by the algorithm. Note that the
order in which the clusters are identified is
not an indicator of merit.
A column is included in the report for each of the features
that are specified using the Workload Characterization screen
(see Section 11.5). In the report shown in Figure 11-8, the
columns that are generated are JOBMXNTA, JOBTCBTM, and
JOBNLR.
JOBMXNTA: The average number of tapes allocated by jobs
assigned to the cluster. Note that this
variable is included in the CA MICS database
only if the DVCT option is specified to the
CA MICS Batch and Operations Analyzer. In the
distributed CA MICS system, this option is not
activated.
JOBTCBTM: The average CPU time consumed by jobs assigned
to the cluster.
JOBNLR: The average number of lines printed by jobs
assigned to the cluster.
RADIUS: The radius of the cluster in standard
deviations. Cluster radii vary from a minimum
of 1.0 to a maximum of 3.0 standard deviations.
NO ASSIGN: The number of resource vectors that were
assigned to the cluster. As a rule, the radius
of a cluster is inversely proportional to the
number of resource vectors assigned. More
simply stated, clusters that represent the
highest percentages of the resource vectors are
most likely to be those with the smallest
radii.
PCT: The percent of the resource vectors in the
sample that are represented by the cluster.
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