=== Run information ===

Scheme: weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 2 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10 Relation: Workers Instances: 534 Attributes: 11 Education South Sex Experience Wage Age Race Occupation Sector Marr Union Test mode: evaluate on training data

=== Clustering model (full training set) ===

kMeans ======

Number of iterations: 10 Within cluster sum of squared errors: 1068.8998454793477

Initial starting points (random):

Cluster 0: 10,N,M,30,6.5,46,W,Other,Other,Married,0 Cluster 1: 18,Y,M,14,6.25,38,W,Professional,Other,Married,0

Missing values globally replaced with mean/mode

Final cluster centroids: Cluster# Attribute Full Data 0 1 (534.0) (251.0) (283.0) ======================================================= Education 13.0187 12.4502 13.523 South N N N Sex M M F Experience 17.8221 17.7331 17.9011 Wage 9.0241 9.4236 8.6697 Age 36.8333 36.1833 37.4099 Race W W W Occupation Other Other Professional Sector Other Other Other Marr Married Married Married Union 0.1798 0.2869 0.0848

Time taken to build model (full training data) : 0.06 seconds

=== Model and evaluation on training set ===

Clustered Instances

0 251 ( 47%) 1 283 ( 53%)

1- Perform SimpleKMeans clustering with default parameters (2 clusters). How woulddescribe the two clusters based on the attribute characteristics? Interpret how the identified clusters are different. Which attributes were more important to differentiate the clusters?

2- Perform EM clustering with 2 clusters. How would describe the two clusters based on the attribute characteristics? How are these two clusters different from the earlier two clusters created in the first question?

3- Perform EM clustering with the default parameters (numClusters = -1). Explain what subsets of the population each cluster represents.