Clustering CE-324: Modern Information Retrieval Sharif University of Technology

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1 Clustering CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)

2 Ch. 16 What is clustering? Clustering: grouping a set of objects into similar ones Docs within a cluster should be similar. Docs from different clusters should be dissimilar. The commonest form of unsupervised learning Unsupervised learning learning from raw data, as opposed to supervised data where a classification of examples is given A common and important task that finds many applications in IR and other places 2

3 Ch. 16 A data set with clear cluster structure How would you design an algorithm for finding the three clusters in this case? 3

4 Applications of clustering in IR 5

5 Search result clustering 6

6 7 yippy.com grouping search results

7 Yahoo! Hierarchy (30) agriculture biology physics CS space dairy crops botany cell forestry agronomy evolution magnetism relativity AI HCI courses craft missions Yahoo! Hierarchy isn t clustering but is the kind of output you want from clustering 8

8 Google News: automatic clustering gives an effective news presentation metaphor 9

9 Sec Scatter/Gather Scatter and gather approach instead of searching by entering terms 10

10 For visualizing a doc collection and its themes Wise et al, Visualizing the non-visual PNNL ThemeScapes, Cartia [Mountain height = cluster size] 11

11 Sec For improving search recall Cluster hypothesis: Docs 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 Query car: also return docs containing automobile Because clustering grouped together docs containing car with those containing automobile. Why might this happen? 12

12 Sec Issues for clustering Representation for clustering Doc representation Vector space? Normalization? Centroids aren t length normalized Need a notion of similarity/distance How many clusters? Fixed a priori? Completely data driven? Avoid trivial clusters - too large or small too large: for navigation purposes you've wasted an extra user click without whittling down the set of docs much 13

13 Notion of similarity/distance Ideal: semantic similarity. Practical: term-statistical similarity We will use cosine similarity. For many algorithms, easier to think in terms of a distance (rather than similarity) We will mostly speak of Euclidean distance But real implementations use cosine similarity 14

14 Clustering algorithms categorization Flat algorithms (k-means) Usually start with a random (partial) partitioning Refine it iteratively Hierarchical algorithms Bottom-up, agglomerative Top-down, divisive 15

15 Hard vs. soft clustering Hard clustering: Each doc belongs to exactly one cluster More common and easier to do Soft clustering:a doc can belong to more than one cluster. 16

16 Partitioning algorithms Construct a partition of N docs into K clusters Given: a set of docs and the number K Find: a partition of docs into K clusters that optimizes the chosen partitioning criterion Finding a global optimum is intractable for many objective functions of clustering Effective heuristic methods: K-means and K-medoids algorithms 17

17 Sec K-means Assumes docs are real-valued vectors x (1),, x (N). Clusters based on centroids (aka the center of gravity or mean) of points in a cluster: μ j = 1 C j x (i) C j x (i) K-means cost function: J(C) = K j=1 x (i) C j x (i) μ j 2 C = {C 1, C 2,, C K } C j : the set of data points assigned to j-th cluster 18

18 Sec K-means algorithm Select K random points {μ 1, μ 2, μ K } as clusters initial centroids. Until clustering converges (or other stopping criterion): For each doc x (i) : Assign x (i) to the cluster C j such that dist(x (i), μ j ) is minimal. For each cluster C j μ j = i C j x(i) C j Reassignment of instances to clusters is based on distance to the current cluster centroids (can equivalently be in terms of similarities) 19

19 20 [Bishop]

20 21

21 Sec Termination conditions Several possibilities for terminal condition, e.g., A fixed number of iterations Doc partition unchanged J < θ: cost function falls below a threshold J < θ: the decrease in the cost function (in two successive iterations) falls below a threshold 22

22 Sec Convergence of K-means K-means algorithm ever reaches a fixed point in which clusters don t change. We must use tie-breaking when there are samples with the same distance from two or more clusters (by assigning it to the lower index cluster) 23

23 K-means decreases J(C) in each iteration (before convergence) Sec First, reassignment monotonically decreases J(C) since each vector is assigned to the closest centroid. Second, recomputation monotonically decreases each i C k x (i) μ k 2 : i Ck x (i) μ k 2 reaches minimum for μ k = 1 C k i C k x (i) K-means typically converges quickly 24

24 Sec Time complexity of K-means Computing distance between two docs: O(M) M is the dimensionality of the vectors. Reassigning clusters: O(KN) distance computations O(KNM). Computing centroids: Each doc gets added once to some centroid: O(NM). Assume these two steps are each done once for I iterations: O(IKNM). 25

25 Sec Seed choice Results can vary based on random selection of initial centroids. Some initializations get poor convergence rate, or convergence to sub-optimal clustering Try out multiple starting points Select good seeds using a heuristic (e.g., doc least similar to any existing mean) Initialize with the results of another method. Example showing sensitivity to seeds 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} 26

26 Sec K-means issues, variations, etc. Computes the centroid only after all points are reassigned Instead, we can re-compute the centroid after every assignment It can improve speed of convergence of K-means Assumes clusters are spherical in vector space Sensitive to coordinate changes, weighting etc. Disjoint and exhaustive Doesn t have a notion of outliers by default But can add outlier filtering Dhillon et al. ICDM 2002 variation to fix some issues with small document clusters 27

27 How many clusters? Number of clusters K is given Partition n docs into predetermined number of clusters Finding the right number is part of the problem Given docs, partition into an appropriate no. of subsets. E.g., for query results - ideal value of K not known up front - though UI may impose limits. 28

28 How many clusters? How many clusters? Six Clusters Two Clusters Four Clusters 29

29 Selecting k 30

30 K not specified in advance Tradeoff between having better focus within each cluster and having too many clusters Solve an optimization problem: penalize having lots of clusters application dependent e.g., compressed summary of search results list. k = min k J min k + λk J min k : show the minimum value of J {C 1,C 2,,C k } obtained in e.g. 100 runs of k-means (with different initializations) 31

31 Penalize lots of clusters Benefit for a doc: cosine similarity to its centroid Total Benefit: sum of the individual doc Benefits. Why is there always a clustering of Total Benefit n? For each cluster, we have a Cost C. For K clusters, the Total Cost is KC. Value of a clustering = Total Benefit - Total Cost. Find clustering of highest value, over all choices of K. Total benefit increases with increasing K. But can stop when it doesn t increase by much. The Cost term enforces this. 32

32 Sec What is a good clustering? Internal criterion: intra-class (that is, intra-cluster) similarity is high inter-class similarity is low The measured quality of a clustering depends on both the doc representation and the similarity measure 33

33 Sec External criteria for clustering quality Quality: ability to discover some or all of patterns in gold standard data Assesses a clustering with respect to ground truth requires labeled data C gold standard classes C 1,, C c, while clustering produces K clusters, ω 1, ω 2,, ω K. 34

34 External criteria Purity Rand Index F measure Normalized Mutual Information (NMI) 35

35 Sec External evaluation of cluster quality Purity: ratio between the dominant class in the cluster ω i and the size of cluster ω i max ω i C j j=1,,c purity ω i = ω i TotalPurity ω 1,, ω c = 1 N i=1 K max j=1,,c ω i C j Biased because having N clusters maximizes purity 36

36 Sec Purity example Cluster I Cluster II Cluster III Cluster I: Purity = 1/6 (max(5, 1, 0)) = 5/6 Cluster II: Purity = 1/6 (max(1, 4, 1)) = 4/6 Cluster III: Purity = 1/5 (max(2, 0, 3)) = 3/5 Total Purity = 1/17 (5+4+3) = 12/17 37

37 Sec Rand Index (RI) measures between pair decisions RI = 0.68 Number of points Same Cluster in clustering Different Clusters in clustering Same class in ground truth Different classes in ground truth

38 Sec Rand index and F-measure RI TP TN TP FP TN FN Compare with standard Precision and Recall: P TP TP FP R TP TP TN People also define and use a cluster F-measure, which is probably a better measure. 39 F β = β2 + 1 PR β 2 P + R

39 Ch. 17 Hierarchical clustering Build a tree-based hierarchical taxonomy (dendrogram) from a set of docs. animal vertebrate fish reptile amphib. mammal invertebrate worm insect crustacean One approach: recursive application of a partitional clustering algorithm. 40

40 Hierarchical clustering 41

41 Dendrogram: hierarchical clustering Clustering obtained by cutting the dendrogram at a desired level: Each connected component forms a cluster. 42

42 Hierarchical Agglomerative Clustering (HAC) Sec Starts with each doc in a separate cluster then repeatedly joins the closest pair of clusters, until there is only one cluster. The history of merging forms a binary tree or hierarchy. Note: the resulting clusters are still hard and induce a partition 43

43 Example Hierarchical Agglomerative Clustering (HAC)

44 45

45 Sec Closest pair of clusters Many variants to defining closest pair of clusters Single-link Similarity of the most similar pair (single-link) Complete-link Similarity of the furthest points, the least similar pair Centroid Clusters whose centroids (centers of gravity) are the most similar Average-link Average similarities between pairs of elements 46

46 Distances between cluster pairs Single-link Complete-link Centroid Average-link 47

47 Sec Single link Use maximum similarity of pairs: sim ( C, C ) max sim ( x, y ) Can result in straggly (long and thin) clusters due to chaining effect. i j x C i, y C After merging C i and C j, the similarity of the resulting cluster to another cluster, C k : sim (( C C ), C ) max( sim ( C, C ), sim ( C, C )) i j k i k j k j 48

48 Sec Single link: example 49

49 Sec Complete link Use minimum similarity of pairs: sim ( C, C ) min sim ( x, y ) i j x C, y C i Makes tighter, spherical clusters that are typically preferable. After merging C i and C j, the similarity of the resulting cluster to another cluster, C k : sim (( C C ), C ) min( sim ( C, C ), sim ( C, C )) i j k i k j k j 50

50 Sec Complete link: example 51

51 Sec Group average Similarity of two clusters = average similarity of all pairs within merged cluster. 1 sim ( C i, C j) sim ( x, y ) C C ( C C 1) x ( C i C j ) y ( C i C j ): y x i j i j Compromise between single and complete link. 52

52 Sec Computing group average similarity When we use dot product as the similarity measure we can compute similarity of two clusters in constant time if we maintain sum of vectors in each cluster. Compute similarity of clusters: s j x C j x If the length of doc vectors are assumed one sim ( c, c ) i j ( s s ) ( s s ) ( C C ) i j i j i j ( C C )( C C 1) i j i j 53

53 Sec Computational complexity First iteration: all HAC methods need to compute similarity of all pairs of instances, O(N 2 ). N 1 merging iterations: compute the distance between the most recently created cluster and all other existing ones. In order to maintain an overall O(N 2 ) performance, computing similarity to each other cluster must be done in constant time. Often O(N 3 ) if done naively or O(N 2 log N) if done more cleverly 54

54 55

55 Cluster Labeling 56

56 Final word and resources In clustering, clusters are inferred from the data without human input (unsupervised learning) However, in practice, it s a bit less clear many ways of influencing the outcome of clustering: number of clusters, similarity measure, representation of docs,... Resources IIR 16 except 16.5 IIR

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