1. A Discriminative Framework for Clustering via Similarity Functions Maria-Florina Balcan Carnegie Mellon University Joint with Avrim Blum and Santosh Vempala

2. 2 Brief Overview of the Talk Vague, difficult to reason about at a general technical level. Supervised Learning Good theoretical models: Clustering Lack of good unified models. Learning from labeled data. Learning from unlabeled data. PAC, SLT Kernels & Similarity fns A PAC-style framework Our work: fix the problem

3. 3 Clustering: Learning from Unlabeled Data [documents] [topic] S set of n objects. 9 ground truth clustering. Goal: h of low error where x, l (x) in {1,…,t}. err(h) = min  Pr x~S [  (h(x))  l (x)] Problem: unlabeled data only! But have a Similarity Function! [sports] [fashion]

4. 4 Clustering: Learning from Unlabeled Data [sports] [fashion] 9 ground truth clustering for S i.e., each x in S has l (x) in {1,…, t }. The similarity function K has to be related to the ground-truth. Input S, a similarity function K. Output Clustering of small error. Protocol

5. 5 Clustering: Learning from Unlabeled Data [sports] [fashion] What natural properties on a similarity function would be sufficient to allow one to cluster well? Fundamental Question

6. 6 Contrast with Standard Approaches Approximation algorithms - analyze algs to optimize various criteria over edges - score algs based on apx ratios Input: graph or embedding into R d Much better when input graph/ similarity is based on heuristics. Mixture models Clustering Theoretical Frameworks Our Approach Discriminative, not generative. Input: embedding into R d - score algs based on error rate - strong probabilistic assumptions Input: graph or similarity info - score algs based on error rate - no strong probabilistic assumptions E.g., clustering documents by topic, web search results by category

7. 7 [sports] [fashion] Condition that trivially works. K(x,y) > 0 for all x,y, l (x) = l (y). K(x,y) 0 for all x,y, l (x) = l (y). K(x,y) < 0 for all x,y, l (x)  l (y). C C’ AA’ What natural properties on a similarity function would be sufficient to allow one to cluster well?

8. 8 Problem: same K can satisfy it for two very different, equally natural clusterings of the same data! All x more similar to all y in own cluster than any z in any other cluster sports fashion soccer tennis Lacoste Gucci sports fashion soccer tennis Lacoste Gucci K(x,x’)=1 K(x,x’)=0.5 K(x,x’)=0 What natural properties on a similarity function would be sufficient to allow one to cluster well?

9. 9 Relax Our Goals 1. Produce a hierarchical clustering s.t. correct answer is approximately some pruning of it.

10. 10 Relax Our Goals soccer tennis Lacoste Gucci 1. Produce a hierarchical clustering s.t. correct answer is approximately some pruning of it. soccer sportsfashion Gucci tennis Lacoste All topics 2. List of clusterings s.t. at least one has low error. Tradeoff strength of assumption with size of list. Obtain a rich, general model.

11. 11 Strict Separation Property Single-Linkage. merge “parts” whose max similarity is highest. Sufficient for hierarchical clustering (If K is symmetric) soccer sportsfashion Gucci tennis Lacoste All topics All x more similar to all y in own cluster than any z in any other cluster sports fashion soccer tennis Lacoste 1 0.5 0 Gucci Algorithm

12. 12 Strict Separation Property Use Single-Linkage, construct a tree s.t. ground-truth clustering is a pruning of the tree. Theorem All x more similar to all y in own cluster than any z in any other cluster If use c-approx. alg. to objective f (e.g, k-median) to minimize error rate, then implicit assumption: Most points (1-O(  ) fraction) satisfy Strict Separation. Clusterings within factor c of optimal are  -close to the target. Incorporate Approximation Assumptions in Our Model Can still cluster well in the tree model. k-median, k-means

13. 13 Stability Property Sufficient for hierarchical clustering Merge “parts” whose average similarity is highest. Single linkage fails, but average linkage works. Neither A or A’ more attracted to the other one than to the rest of its own cluster. For all C, C’, all A ½ C, A’ µ C’, K(A,C-A) > K(A,A’) (K(A,A’) - average attraction between A and A’) A A’ C C’

14. 14 Stability Property K(P 1,P 3 ) ¸ K(P 1,C-P 1 ) and K(P 1,C-P 1 ) > K(P 1,P 2 ). All “parts” laminar wrt target clustering. Contradiction. Analysis: Use Average Linkage, construct a tree s.t. the ground-truth clustering is a pruning of the tree. Theorem Failure iff merge P 1, P 2 s.t. P 1 ½ C, P 2 Å C = . But must exist P 3 ½ C s.t. P1P1 P2P2 P3P3 C (K(A,A’) - average attraction between A and A’) A A’ C C’ For all C, C’, all A ½ C, A’ µ C’, K(A,C-A) > K(A,A’)

15. 15 Stability Property (K(A,A’) - average attraction between A and A’) A A’ C C’ Average Linkage breaks down if K is not symmetric. 0.5 0.25 Instead, run “Boruvka-inspired” algorithm: – Each current cluster C i points to argmax Cj K(C i,C j ) – Merge directed cycles. For all C, C’, all A ½ C, A’ µ C’, K(A,C-A) > K(A,A’)

16. 16 Unified Model for Clustering Algorithm A 1 … … … Property P 1 Property P i Property P n Algorithm A 2 Algorithm A m of the similarity function wrt the ground-truth clustering Question 1: Given a property of the similarity function w.r.t. ground truth clustering, what is a good algorithm?

17. 17 Unified Model for Clustering Algorithm A 1 … … … Property P 1 Property P i Property P n Algorithm A 2 Algorithm A m of the similarity function wrt the ground-truth clustering Question 2: Given the algorithm, what property of the similarity function w.r.t. ground truth clustering should the expert aim for?

18. 18 Other Examples of Properties and Algorithms A A’ C C’ Sufficient for hierarchical clustering Find hierarchy using a multi-stage learning-based algorithm. Average Attraction Property Not sufficient for hierarchical clustering Can produce a small list of clusterings.(sampling based algorithm) Stability of Large Subsets Property Upper bound: t O(t/  2 log t/  ) Lower bound: t O(1/  ) E x’ 2 C(x) [K(x,x’)] > E x’ 2 C’ [K(x,x’)]+  E x’ 2 C(x) [K(x,x’)] > E x’ 2 C’ [K(x,x’)]+  ( 8 C’  C(x)) For all clusters C, C’, for all A µ C, A’ µ C, |A|+|A’| ¸ sn, neither A nor A’ more attracted to the other one than to the rest of its own cluster.

19. 19 1)Generate list L of candidate clusters (average attraction alg.) 2)For every (C, C 0 ) in L s.t. all three parts are large: 3) Clean and hook up the surviving clusters into a tree. If K(C Å C 0, C \ C 0 ) ¸ K(C Å C 0, C 0 \ C), then throw out C 0 C C0C0 C Å C 0 Ensure that any ground-truth cluster is f-close to one in L. Else throw out C. Clustering A A’ Algorithm C C’ For all C, C’, all A ½ C, A’ µ C’, K(A,C-A) > K(A,A’) |A|+|A’| ¸ sn Stability of Large Subsets Property

20. 20 Stability of Large Subsets A For all C, C’, all A ½ C, A’ µ C’, |A|+|A’| ¸ sn K(A,C-A) > K(A,A’)+  Clustering A’ C C’ If s=O(  2 /k 2 ), f=O(  2  /k 2 ), then produce a tree s.t. the ground-truth is  -close to a pruning. Theorem

21. 21 The Inductive Setting Insert new points as they arrive. Draw sample S, cluster S (in the list or tree model). Inductive Setting Many of our algorithms extend naturally to this setting. instance space X Sample S xx xx To get poly time for stab of all subsets, need to argue that sampling preserves stability. [AFKK]

22. 22 Similarity Functions for Clustering, Summary Natural conditions on K to be useful for clustering. For robust theory, relax objective: hierarchy, list. Algos for stability of large subsets; -strict separation. Algos and analysis for the inductive setting. Main Conceptual Contributions Technically Most Difficult Aspects A general model that parallels PAC, SLT, Learning with Kernels and Similarity Functions in Supervised Classification.

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24. 24 Properties Summary PropertyModel, AlgorithmClustering Complexity Strict SeparationHierarchical, Linkage based  2 t ) Stability, all subsets. (Weak, Strong, etc) Hierarchical, Linkage based  2 t ) Average Attraction (Weighted) List, Sampling based & NN t O( t /  2 ) Stability of large subsets (SLS) Hierarchical, complex algorithm (running time t O( t /  2 ) )  2 t )  strict separationHierarchical  2 t ) (2,  ) k-medianspecial case of strict separation, =3 

25. 25 Algorithm sketch: 1.For each x,m generate cluster of m most similar pts to x. Delete any of size < 4  n. 2.If two clusters like this: have each y in intersection choose based on median similarity to C-C’, C’-C. Thm: If 9 bad set B of ·  n pts s.t. S’ = S-B satisfies strict ordering, and if all clusters of size ¸ 5  n, then can find tree of error · . > 2  n C C’

26. 26 Algorithm sketch: 1.For each x,m generate cluster of m most similar pts to x. Delete any of size < 4  n. 3.If two clusters like this: have each y in C M C’ choose in or out based on  n+1 st most similar in C Å C’ or S-(C [ C’) Thm: If 9 bad set B of ·  n pts s.t. S’ = S-B satisfies strict ordering, and if all clusters of size ¸ 5  n, then can find tree of error · . < 2  n C C’

27. 27 Algorithm sketch: 1.For each x,m generate cluster of m most similar pts to x. Delete any of size < 4  n. 4.If two clusters like this: have each y in C-C’ choose in or out based on  n+1 st most similar in C Å C’ or S-(C [ C’) Thm: If 9 bad set B of ·  n pts s.t. S’ = S-B satisfies strict ordering, and if all clusters of size ¸ 5  n, then can find tree of error · . < 2  n> 2  n C C’

28. 28 Algorithm sketch: 1.For each x,m generate cluster of m most similar pts to x. Delete any of size < 4  n. 5.Then argue that never hurts correct clusters (wrt S’) and each step makes progress. Thm: If 9 bad set B of ·  n pts s.t. S’ = S-B satisfies strict ordering, and if all clusters of size ¸ 5  n, then can find tree of error · .