Clustering

1. Clustering Paolo Ferragina Dipartimento di Informatica Università di Pisa This is a mix of slides taken from several presentations, plus my touch !

2. Objectives of Cluster Analysis • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Competing objectives Intra-cluster distances are minimized Inter-cluster distances are maximized The commonest form of unsupervised learning

3. Google News: automatic clustering gives an effective news presentation metaphor

4. Sec. 16.1 For improving search recall • Cluster hypothesis - Documents in the same cluster behave similarly with respect to relevance to information needs • Therefore, to improve search recall: • Cluster docs in corpus a priori • When a query matches a doc D, also return other docs in the cluster containing D • Hope if we do this: The query “car” will also return docs containing automobile Butalsoforspeeding up the searchoperation

5. Sec. 16.1 For better visualization/navigation of search results

6. Sec. 16.2 Issues for clustering • Representation for clustering • Document representation • Vector space? Normalization? • Need a notion of similarity/distance • How many clusters? • Fixed a priori? • Completely data driven?

7. Notion of similarity/distance • Ideal: semantic similarity • Practical: term-statistical similarity • Docs as vectors • We will use cosine similarity. • For many algorithms, easier to think in terms of a distance (rather than similarity) between docs.

8. Clustering Algorithms • Flat algorithms • Create a set of clusters • Usually start with a random (partial) partitioning • Refine it iteratively • K means clustering • Hierarchical algorithms • Create a hierarchy of clusters (dendogram) • Bottom-up, agglomerative • Top-down, divisive

9. Hard vs. soft clustering • Hard clustering: Each document belongs to exactly one cluster • More common and easier to do • Soft clustering: Each document can belong to more than one cluster. • Makes more sense for applications like creating browsable hierarchies • News is a proper example • Search results is another example

10. Flat & Partitioning Algorithms • Given: a set of n documents and the number K • Find: a partition in K clusters that optimizes the chosen partitioning criterion • Globally optimal • Intractable for many objective functions • Ergo, exhaustively enumerate all partitions • Locally optimal • Effective heuristic methods: K-means and K-medoids algorithms

11. Sec. 16.4 K-Means • Assumes documents are real-valued vectors. • Clusters based on centroids (aka the center of gravity or mean) of points in a cluster, c: • Reassignment of instances to clusters is based on distance to the current cluster centroids.

12. Sec. 16.4 K-Means Algorithm Select K random docs {s1, s2,… sK} as seeds. Until clustering converges (or other stopping criterion): For each doc di: Assign di to the cluster crsuch that dist(di, sr) is minimal. For each cluster cj sj = (cj)

13. Sec. 16.4 Pick seeds Reassign clusters Compute centroids Reassign clusters x x Compute centroids x x x x K Means Example (K=2) Reassign clusters Converged!

14. Sec. 16.4 Termination conditions • Several possibilities: • A fixed number of iterations. • Doc partition unchanged. • Centroid positions don’t change.

15. Sec. 16.4 Convergence • Why should the K-means algorithm ever reach a fixed point? • K-means is a special case of a general procedure known as the Expectation Maximization (EM) algorithm • EM is known to converge • Number of iterations could be large • But in practice usually isn’t

16. Sec. 16.4 Convergence of K-Means • Define goodness measure of cluster c as sum of squared distances from cluster centroid: • G(c,s)= Σj in c (dj – sc)2 (sum over all di in cluster c) • G(C,s) = Σc G(c,s) • Reassignment monotonically decreases G • It is a coordinate descent algorithm (optimize one component at a time) • At any step we have some value for G(C,s) 1) Fix s, optimize C  assign doc to the closest centroid  G(C’,s) < G(C,s) 2) Fix C’, optimize s  take the new centroids  G(C’,s’) < G(C’,s) < G(C,s) The new cost is smaller than the original one  local minimum

17. Sec. 16.4 Time Complexity • The centroids are K • Each doc/centroid consists of M dimensions • Computing distance btw vectors is O(M) time. • Reassigning clusters: Each doc compared with all centroids, O(N x K x M) time. • Computing centroids: Each doc gets added once to some centroid, O(NM) time. Assume these two steps are each done once for I iterations: O(IKNM).

18. Sec. 16.4 Seed Choice • Results can vary based on random seed selection. • Some seeds can result in poor convergence rate, or convergence to sub-optimal clusterings. • Select good seeds using a heuristic • doc least similar to any existing centroid • According to a probability distribution that depends inversely-proportional on the distance from the other current centroids Example showing sensitivity to seeds In the above, if you start with B and E as centroids you converge to {A,B,C} and {D,E,F} If you start with D and F you converge to {A,B,D,E} {C,F}

19. How Many Clusters? • Number of clusters K is given • Partition n docs into predetermined number of clusters • Finding the “right” number of clusters is part of the problem • Can usually take an algorithm for one flavor and convert to the other.

20. Bisecting K-means Variant of K-means that can produce a partitional or a hierarchical clustering SSE = G(C,s) is called Sum of Squared Error

21. Bisecting K-means Example

22. K-means Pros • Simple • Fast for low dimensional data • It can find pure sub-clusters if large number of clusters is specified (but, over-partitioning) Cons • K-Means cannot handle non-globular data of different sizes and densities • K-Means will not identify outliers • K-Means is restricted to data which has the notion of a center (centroid)

23. Ch. 17 animal vertebrate invertebrate fish reptile amphib. mammal worm insect crustacean Hierarchical Clustering • Build a tree-based hierarchical taxonomy (dendrogram) from a set of documents • Possibility: recursive application of a partitional clustering algorithm

24. Strengths of Hierarchical Clustering • No assumption of any particular number of clusters • Any desired number of clusters can be obtained by ‘cutting’ the dendogram at the proper level • They may correspond to meaningful taxonomies • Example in biological sciences (e.g., animal kingdom, phylogeny reconstruction, …)

25. Sec. 17.1 Hierarchical Agglomerative Clustering (HAC) • Starts with each doc in a separate cluster • Then repeatedly jointhe closest pairof clusters, until there is only one cluster. • The history of merging forms a binary tree or hierarchy.

26. Sec. 17.2 Closest pair of clusters • Single-link • Similarity of the closest points, the most cosine-similar • Complete-link • Similarity of the farthest points, the least cosine-similar • Centroid • Clusters whose centroids are the closest (or most cosine-similar) • Average-link • Clusters whose average distance/cosine between pairs of elements is the smallest

27. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity Similarity? • Single link (MIN) • Complete link (MAX) • Centroids • Average Proximity Matrix

28. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Centroids • Average Proximity Matrix

29. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Centroids • Average Proximity Matrix

30. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to define Inter-Cluster Similarity   • MIN • MAX • Centroids • Average Proximity Matrix

31. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Centroids • Average Proximity Matrix

32. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . Starting Situation • Start with clusters of individual points and a proximity matrix Proximity Matrix

33. C1 C2 C3 C4 C5 C1 C2 C3 C4 C5 Intermediate Situation • After some merging steps, we have some clusters C3 C4 C1 Proximity Matrix C1 C3 C2 C5 C4 C5 C2

34. C1 C2 C3 C4 C5 C1 C2 C3 C4 C5 Intermediate Situation • We want to merge the two closest clusters (C2 and C5) and update the proximity matrix. C3 C4 C1 Proximity Matrix C1 C3 C2 U C5 C4 C5 C2

35. After Merging • The question is “How do we update the proximity matrix?” C2 U C5 C1 C3 C4 C1 ? C3 ? ? ? ? C2 U C5 C4 C3 ? ? C4 C1 Proximity Matrix C2 U C5 C3 C4 C1 C2 U C5

36. 1 2 3 4 5 Cluster Similarity: MIN or Single Link • Similarity of two clusters is based on the two most similar (closest) points in the different clusters • Determined by one pair of points, i.e., by one link in the proximity graph. ?

37. Two Clusters Strength of MIN Original Points • Can handle non-elliptical shapes

38. Two Clusters Limitations of MIN Original Points • Sensitive to noise and outliers

39. 1 2 3 4 5 Cluster Similarity: MAX or Complete Linkage • Similarity of two clusters is based on the two least similar (most distant) points in the different clusters • Determined by all pairs of points in the two clusters ?

40. Two Clusters Strength of MAX Original Points • Less susceptible to noise and outliers

41. Two Clusters Limitations of MAX Original Points • Tends to break large clusters • Biased towards globular clusters

42. 1 2 4 5 3 Cluster Similarity: Average • Proximity of two clusters is the average of pairwise proximity between points in the two clusters. ?

43. 5 1 5 3 1 4 1 2 5 2 5 2 1 5 5 2 2 2 3 6 3 6 3 6 3 3 1 4 4 1 4 4 4 Hierarchical Clustering: Comparison MAX MIN Average

44. Sec. 16.3 How to evaluate clustering quality ? Assesses a clustering with respect to ground truth … requires labeled data Produce the gold standard is costly !!