1. Cluster Evaluation Metrics that can be used to evaluate the quality of a set of document clusters.

2. Precision Recall & FScore  From Zhao and Karypis, 2002  These metrics are computed for every (class,cluster) pair.  Terms:  class L r of size n r  cluster S i if size n i  n ri documents in S i from class L r

3. Precision  Loosely equated to accuracy  Roughly answers the question: “How many of the documents in this cluster belong there?”  P(L r, S i ) = n ri /n i

4. Recall  Roughly answers the question: “Did all of the documents that belong in this cluster make it in?”  P(L r, S i ) = n ri /n r

5. FScore  Harmonic Mean of Precision and Recall  Tries to give a good combination of the other 2 metrics  Calculated with the equation:

6. FScore - Entire Solution  We calculate a per-class FScore:  We then combine these scores into a weighted average:

7. FScore Caveats  The Zhao, Karypis paper focused on Hierarchical clustering, so the definitions of Precision/Mean and FScore might not apply as well to “flat” clustering.  The metrics rely on the use of class labels, so they can not be applied in situations were there is no labeled data.

8. Possible Modifications  Calculate a per-cluster (not per class FScore:  Combine these scores into a weighted average:

9. Rand Index  Yeung, et al., 2001  Measure of partition agreement  Answers the question “How similar are these two ways of partitioning the data?”  To evaluate clusters, we compute the Rand Index between actual labels and clusters

10. Rand Index  a = # pairs of documents that are in the same S i and L r  b = # pairs of documents that are in the same L r, but not the same S i  c = # pairs of documents in the same S i, but not the same L r  d = # pairs of documents that are not in the same L r nor S i.

11. Adjusted Rand Index  The Rand index has a problem, the expected value for any 2 random partitions is relatively high, we’d like it to be close to 0.  Adjusted Rand index puts the expected value at 0, gives a more dynamic range and is probably a better metric.  See appendix B of Yeung, et al., 2001.

12. Rand Index Caveat  Penalizes good, but finer grained clusters: imagine a sports class that produces 2 clusters, one for ball sports and one for track sports.  To fix that issue, we could hard label each cluster and treat all clusters with the same label as the same (clustering the clusters).

13. Problems  The metrics so far depend on class labels.  They also give undeserved high scores as k approaches n, because almost all instances end up alone in a cluster.

14. Label Entropy  My idea? (I haven’t seen it anywhere else)  Calculate an entropy value per cluster:  Combine entropies (weighted average):

15. Log Likelihood of Data  Calculate the log likelihood of the Data according to the clusterers model.  If the clusterer doesn’t have an explicit model, treat clusters as classes and train a class conditional model of the data based on these class labelings. Use the new model to calculate log likelihood.