Clustering: Classic Methods and Modern Views

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1 Clustering: Classic Methods and Modern Views Marina Meilă University of Washington June 22, 2015 Lorentz Center Workshop on Clusters, Games and Axioms

2 Outline Paradigms for clustering Basic algorithms K-means clustering and the quadratic distortion Model based / soft clustering, the EM algorihtm and maximizing likelihood Similarity based / graph clustering and the Spectral clustering algorithm Further issues and current trends

3 Outline Paradigms for clustering Basic algorithms K-means clustering and the quadratic distortion Model based / soft clustering, the EM algorihtm and maximizing likelihood Similarity based / graph clustering and the Spectral clustering algorithm Further issues and current trends

4 What is clustering? Problem and Notation Informal definition Clustering = Finding groups in data Notation D = {x 1, x 2,... x n} a data set n = number of data points K = number of clusters (K << n) C = {C 1, C 2,..., C K } a partition of D into disjoint subsets k(i) = the label of point i L(C) = cost (loss) of C (to be minimized) Second informal definition Clustering = given n data points, separate them into K clusters Hard vs. soft clusterings Hard clustering C: an item belongs to only 1 cluster Soft clustering γ = {γ ki } i=1:n k=1:k γ ki = the degree of membership of point i to cluster k γ ki = 1 for all i (usually associated with a probabilistic model) k

5

6 Paradigms Depend on type of data, type of clustering, type of cost (probabilistic or not), and constraints (about K, shape of clusters) Data = vectors {x i } in R d Parametric Cost based [hard] (K known) Model based [soft] Non-parametric Dirichlet process mixtures [soft] (K determined Information bottleneck [soft] by algorithm) Modes of distribution [hard] Gaussian blurring mean shift [hard] Data = similarities between pairs of points [S ij ] i,j=1:n, S ij = S ji 0 (called Similarity based clustering) Graph partitioning spectral clustering [hard, K fixed, cost based] typical cuts [hard non-parametric, cost based] Affinity propagation [hard/soft non-parametric]

7 Classification vs Clustering Classification Clustering Performance criterion Expected error a wide variety Supervised Unsupervised Generalization Performance on new Performance on current data is what matters data is what matters K Known Unknown Goal Prediction Exploration, etc Stage of field Mature Young: new paradigms and theoretical results are emerging

8 Outline Paradigms for clustering Basic algorithms K-means clustering and the quadratic distortion Model based / soft clustering, the EM algorihtm and maximizing likelihood Similarity based / graph clustering and the Spectral clustering algorithm Further issues and current trends

9 K-means clustering Algorithm K-Means Input Data D = {x i } i=1:n, number clusters K nitialize centers µ 1, µ 2,... µ K R d at random Iterate until convergence 1. for i = 1 : n (assign points to clusters new clustering) k(i) = argmin x i µ k k 2. for k = 1 : K (recalculate centers) µ k = 1 x i (1) C k i C k Convergence if C doesn t change at iteration m it will never change after that convergence in finite number of steps to local optimum of cost L (defined next) therefore, initialization will matter

10 The K-means cost L(C) = K k=1 i C k x i µ k 2 K-means solves a least-squares problem the cost L is called quadratic distortion Proposition The K-means algorithm decreases L(C) at every step. Sketch of proof step 1: reassigning the labels can only decrease L step 2: reassigning the centers µ k can only decrease L because µ k as given by (1) is the solution to µ k = min x i µ 2 (2) µ R d i C k

11 The K-means cost, rewritten L as sum of squared distances to centers K L(C) = x i µ k 2 k=1 i C k L as sum of squared intracluster distances K 1 L(C) = x i x j 2 (3) C k k=1 i,j C k L in matrix form Define the assignment matrix associated with C by Z(clust) Let C = {C 1 = {1, 2, 3}, C 2 = {4, 5}} Z unnorm (C) = C 1 C point i Z(C) = 0 1 Then Z is an orthogonal matrix (columns are orthornormal) and Proof of (3): Replace µ C 1 C L(C) = trace Z T AZ with A ij = x i x j 2 (4) as expressed in (1) in the expression of L, then rearrange the terms

12 Initialization of the centroids µ 1:K Idea 1: start with K points at random Idea 2: start with K data points at random What s wrong with chosing K data points at random? The probability of hitting all K clusters with K samples approaches 0 when K > 5 Idea 3: start with K data points using Fastest First Traversal (greedy simple approach to spread out centers) Idea 4: k-means++ (randomized, theoretically backed approach to spread out centers) Idea 5: K-logK Initialization (start with enough centers to hit all clusters, then prune down to K)

13 The K-logK initialization The K-logK Initialization (see also ) 1. pick µ 0 1:K at random from data set, where K = O(K log K) (this assures that each cluster has at least 1 center w.h.p) 2. run 1 step of K-means 3. remove all centers µ 0 k that have few points, e.g C k < n ek 4. from the remaining centers select K centers by Fastest First Traversal 4.1 pick µ 1 at random from the remaining {µ 0 1:K } 4.2 for k = 2 : K, µ k argmax µ 0 k min j=1:k 1 µ 0 k µ j, i.e next µ k is furthest away from the already chosen centers 5. continue with the standard K-means algorithm

14 K-means clustering with K-logK Initialization Example using a mixture of 7 Normal distributions with 100 outliers sampled uniformly K-logK K = 7, T = 100, n = 1100, c = K means cost iteration 50 Naive K = 7 T = 100, n = K means cost iteration

15 Model based clustering: Mixture models The mixture density f (x) = K π k f k (x) k= f k (x) = the components of the mixture each is a density f called mixture of Gaussians if f k = Normal µk,σ k π k = the mixing proportions, k = 1K π k = 1, π k 0. model parameters θ = (π 1:K, µ 1:K, Σ 1:K ) The degree of membership of point i to cluster k γ ki def = P[x i C k ] = π kf k (x) f (x) for i = 1 : n, k = 1 depends on x i and on the model parameters (5)

16 The Expectation-Maximization (EM) Algorithm Algorithm Expectation-Maximization (EM) Input Data D = {x i } i=1:n, number clusters K nitialize parameters π 1:K R, µ 1:K R d, Σ 1:K R d d Iterate until convergence E step (Optimize clustering) for i = 1 : n, k = 1 : K M step (Optimize parameters) compute γ ki Compute number of points in cluster k m Γ k = γ ki, k = 1 : K Estimate parameters i=1 = π kf k (x) f (x) (note: k Γ k = n) (6) π k = Γ k n, µ k = Σ k = k = 1 : K n γ ki x i Γ i=1 k n i=1 γ ki (x i µ k )(x i µ k ) T Γ k

17 EM versus K-means Alternates between cluster assignments and parameter estimation Cluster assignments γ ki are probabilistic Cluster parametrization more flexible Converges to local optimum of log-likelihood Initialization recommended by K-logK method Modern algorithms with guarantees (for e.g. mixtures of Gaussians) Random projections Projection on principal subspace Two step EM (=K-logK initialization + one more EM iteration)

18 Outline Paradigms for clustering Basic algorithms K-means clustering and the quadratic distortion Model based / soft clustering, the EM algorihtm and maximizing likelihood Similarity based / graph clustering and the Spectral clustering algorithm Further issues and current trends

19 Similarity based clustering Paradigm: the features we observe are measures of similarity/dissimilarity between pairs of data points, e.g points features Image segmentation pixels distance in color space, location, separated by a contour, belong to same texture Social network people friendship, coauthorship, hyperlinks, Text analysis words appear in same context The features are summarized by a single similarity measure S ij e.g S ij = e k α k feature k (i,j) for all points i, j symmetric S ij = S ji non-negative S ij 0 Mathematically, we can see the data as a n n matrix S = [S ij ] a (weighted) graph points = graph nodes, similarity S ij = weight of edge ij meaningful because very few similarities are large Then, clustering is cutting the graph

20 Artificial matrixuw Statistics co-authorshipsimilarity data based useful for R d data as well S ij =# tech-reports co-authored S ij = e x i x j 2 σ 2

21 Criteria for clustering Graph cuts remove some edges = disconnected graph the groups are the connected components By similar behavior nodes i, j in the same group iff i, j have the same pattern of transitions at group level By Embedding map graph nodes {1, 2,..., n} to R K then use standard clustering methods (e.g K-means) By diffusion distance All are equivalent (approximately) when the data is clusterable

22 Spectral clustering in a nutshell

23 Spectral clustering in a nutshell Similarity S Preprocess Top K e-vectors v 1:K Data embedded by v Cluster with K-means P = D 1 S point i

24 Definitions V = {1, 2,..., n} node degree or volume D i = j V S ij volume of cluster C V D C = i C D i cut between subsets C, C V S ij i C j C Multiway Normalized Cut of a partition C = {C 1:K } of V MNCut(C) = K k=1 k k Cut(C k, C k ) D Ck in particular, for K = 2, MNCut(C, C ) = Cut(C, C ) D C D C

25 Spectral clustering algorithm Spectral Clustering Algorithm Input Similarity matrix S, number of clusters K 1. Transform S: Set D i = n j=1 S ij, j = 1 : n the node degrees. Form the transition matrix P = [P ij ] n ij=1 with P ij S ij /D i, for i, j = 1 : n 2. Compute the largest K eigenvalues λ 1 = 1 λ 2... λ K and eigenvectors v 1,... v K of P. 3. Embed the data in principal subspace Let V = [ v 2 v 3... v K ] R n K, x i i-th row of V. 4. (orthogonal initialization) Find K initial centers by 4.1 take µ 1 randomly from x 1,... x n 4.2 for k = 2,... K set µ k = argmin xi max k <k µ T k x i. 5. Run the K-means algorithm on the data x 1:n starting from the centers µ 1:K. An algorithm based on and.

26 Properties of spectral clustering Arbitrary cluster shapes (main advantage) Elegant mathematically Practical up to medium sized problems Running time (by Lanczos algorithm) O(nk)/iteration. Works well when K known, not too large estimating K Depend heavily on the similarity function (main problem) learning the similarities,,, Outliers become separate clusters (user must adjust K accordingly!) Very popular, many variants which aim to improve on the above Diffusion maps : normalize the eigenvectors λ t kv k Practical fix, when K large: only compute a fixed number of eigenvectors d < K. This avoids the effects of noise in lower ranked eigenvectors

27 Understanding spectral clustering Graph cuts Spectral clustering minimizes MNCut(C) = K Cut(C k,c k ) k=1 k k D Ck By similar behavior in the random walk on the graph (not the smallest K-way cut!)

28 Understanding spectral clustering (2) By Spectal Embedding The principal e-vectors v 1:K Z orthogonal matrix with n rows and K columns, which maximizes trace Z T LZ with L = D 1/2 SD 1/2 Compare with K-means cost L(C) = trace Z T AZ + constant with A ij = x i x j 2 item All are equivalent (approximately) when the data is clusterable. Clusterability characterized by

29 Outline Paradigms for clustering Basic algorithms K-means clustering and the quadratic distortion Model based / soft clustering, the EM algorihtm and maximizing likelihood Similarity based / graph clustering and the Spectral clustering algorithm Further issues and current trends

30 Further issues and current trends (an incomplete list) Selecting K many ad-hoc methods BIC (statistical model selection method) for mixture models theoretically unsupported in clustering stability-based selection (a boostrap like method) Idea: if a clustering C is supported by the data then it is stable to perturbations theoretical results only prelimnary, but empirical evidence promising Non-parametric clustering mixture models with unbounded K (known as Dirichlet Process Mixtures, or Bayesian Nonparametric clustering) methods based on a kernel density estimator find the peaks of the density Mean-Shift, Gaussian Blurring Mean-Shift level-set methods (find the high density regions) for similarity data Affinity Propagation Clusterability Algorithms with guarantees for clusterable data Scalable algorithms

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