Clustering using eigenvalues

0.3015 0.1437 0.5697 -0.1370 -0.1031 0.2799 -0.3072

-0.3541 0.0532 -0.3653 0.1530 -0.4354 0.3755 0.3780

0.5030 0.1095 -0.0012 -0.3877 -0.3303 -0.1781 0.4773

-0.1898 0.2677 0.1110 0.4438 -0.2940 -0.6348 -0.2343

-0.0782 -0.2891 -0.4343 -0.5166 0.1834 -0.3544 -0.2806

0.1699 -0.4425 -0.1692 0.2434 -0.1231 0.3790 -0.4082

0.4196 0.1959 -0.2627 0.4372 0.6051 0.0026 0.2179

-0.4610 0.4705 0.0991 -0.2912 0.3664 0.2418 -0.0280

-0.2665 -0.5948 0.4834 0.0926 0.2346 -0.1348 0.4317

8 9

-0.4861 0.3542

-0.3688 0.3248

0.3451 0.3114

0.0767 0.3645

-0.3094 0.3480

0.4772 0.3666

-0.1661 0.2900

0.3895 0.3554

-0.0036 0.2707

Eigenvectors: Consider eigenvectors 3,5,7 what clusters do they suggest?

Eigenvalues

1 99.7244

2 99.6914

3 99.8045

4 99.6000

5 99.8660

6 99.4565

7 100.0190

8 99.2243

9 102.6140

1 2 3 4 5 6 7

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