KMeans Example: NumClusters, UnpackColumns ('false') | Teradata Vantage - KMeans Example: NumClusters, UnpackColumns ('false') - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
9.02
9.01
2.0
1.3
Published
February 2022
Language
English (United States)
Last Update
2022-02-10
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rnn1580259159235.ditamap
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dita:id
B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢

Input

The InputTable has five attributes of personal computers (price, speed, hard disk size, RAM, and screen size). The table has over 6000 rows. These examples use different syntax elements to find eight clusters based on the five attributes.

InputTable: computers_train1
id price speed hd ram screen
1 1499 25 80 4 14
2 1795 33 85 2 14
3 1595 25 170 4 15
4 1849 25 170 8 14
5 3295 33 340 16 14
6 3695 66 340 16 14
7 1720 25 170 4 14
8 1995 50 85 2 14
9 2225 50 210 8 14
12 2605 66 210 8 14
13 2045 50 130 4 14
14 2295 25 245 8 14
16 2225 50 130 4 14
17 1595 33 85 2 14
18 2325 33 210 4 15
19 2095 33 250 4 15
20 4395 66 452 8 14
... ... ... ... ... ...

SQL Call

This call tries to group the 5-dimensional data points into 8 clusters. UnpackColumns is 'false' by default.

SELECT * FROM KMeans (
  ON computers_train1 AS InputTable
  OUT TABLE OutputTable (kmeanssample_centroid)
  USING
  NumClusters (8)
  StopThreshold (0.05)
  MaxIterNum (10)
) AS dt;

Output

 clusterid mean                                                                                 size withinss         
 --------- ------------------------------------------------------------------------------------ ---- ---------------- 
 0         3072.21428571429 52.6428571428571 271.071428571429 8.57142857142857 14.7142857142857 14   586706.785714269
 1         1740.88888888889 30.3333333333333 141.666666666667 4.66666666666667 14.1111111111111 9    51205.7777777761
 2         1481.14285714286 27.2857142857143 112.857142857143 3.14285714285714 14.1428571428571 7    81104.8571428545
 3         1991.0 38.8461538461538 160.384615384615 4.46153846153846 14.0                       13   51006.0000000149
 4         2615.75 41.3333333333333 220.166666666667 7.33333333333333 14.75                     12   140175.50000003 
 5         2371.75 48.75 204.625 6.0 14.25                                                      8    36636.375       
 6         2192.8 38.1 194.4 5.2 14.1                                                           10   41549.3999999911
 7         4026.42857142857 59.0 432.285714285714 9.14285714285714 14.2857142857143             7    324109.428571463
 --------- ------------------------------------------------------------------------------------ ---- ----------------
           Converged : False                                                                                         
           Number of Iterations : 10                                                                                 
           Number of clusters : 8                                                                                    
           Successfully created Output table                                                                         
           Total_WithinSS : 1312494.1242063986                                                                       
           Between_SS : 3.8234387625793636E7
SELECT * FROM kmeanssample_centroid;
 clusterid mean                                                                                 size withinss         
 --------- ------------------------------------------------------------------------------------ ---- ---------------- 
         1 1740.88888888889 30.3333333333333 141.666666666667 4.66666666666667 14.1111111111111    9 51205.7777777761
         3 1991.0 38.8461538461538 160.384615384615 4.46153846153846 14.0                         13 51006.0000000149
         5 2371.75 48.75 204.625 6.0 14.25                                                         8        36636.375
         7 4026.42857142857 59.0 432.285714285714 9.14285714285714 14.2857142857143                7 324109.428571463
         0 3072.21428571429 52.6428571428571 271.071428571429 8.57142857142857 14.7142857142857   14 586706.785714269
         2 1481.14285714286 27.2857142857143 112.857142857143 3.14285714285714 14.1428571428571    7 81104.8571428545
         4 2615.75 41.3333333333333 220.166666666667 7.33333333333333 14.75                       12  140175.50000003
         6 2192.8 38.1 194.4 5.2 14.1                                                             10 41549.3999999911

Download a zip file of all examples and a SQL script file that creates their input tables.