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- 1. Prof. Pier Luca Lanzi Hierarchical Clustering Data Mining andText Mining (UIC 583 @ Politecnico di Milano)
- 2. Prof. Pier Luca Lanzi 2
- 3. Prof. Pier Luca Lanzi 3
- 4. Prof. Pier Luca Lanzi 4
- 5. Prof. Pier Luca Lanzi 5
- 6. Prof. Pier Luca Lanzi 6
- 7. Prof. Pier Luca Lanzi 7
- 8. Prof. Pier Luca Lanzi 8
- 9. Prof. Pier Luca Lanzi 9
- 10. Prof. Pier Luca Lanzi What is Hierarchical Clustering? • Suppose we have five items, a, b, c, d, and e. • Initially, we consider one cluster for each item • Then, at each step we merge together the most similar clusters, until we generate one cluster a b c d e a,b d,e c,d,e a,b,c,d,e Step 0 Step 1 Step 2 Step 3 Step 4 10
- 11. Prof. Pier Luca Lanzi What is Hierarchical Clustering? • Alternatively, we start from one cluster containing the five elements • Then, at each step we split one cluster to improve intracluster similarity, until all the elements are contained in one cluster c a b d e d,e a,b,c,d,e a,b c,d,e Step 4 Step 3 Step 2 Step 1 Step 0
- 12. Prof. Pier Luca Lanzi What is Hierarchical Clustering? • By far, it is the most common clustering technique • Produces a hierarchy of nested clusters • The hiearchy be visualized as a dendrogram: a tree like diagram that records the sequences of merges or splits a b c d e a,b d,e c,d,e a,b,c,d,e 12
- 13. Prof. Pier Luca Lanzi What Approaches? • Agglomerative § Start individual clusters, at each step, merge the closest pair of clusters until only one cluster (or k clusters) left • Divisive § Start with one cluster, at each step, split a cluster until each cluster contains a point (or there are k clusters) 13 a b c d e a,b d,e c,d,e a,b,c,d,e agglomerative divisive
- 14. Prof. Pier Luca Lanzi Strengths of Hierarchical Clustering • No need to assume any particular number of clusters • Any desired number of clusters can be obtained by ‘cutting’ the dendrogram at the proper level • They may correspond to meaningful taxonomies • Example in biological sciences include animal kingdom, phylogeny reconstruction, etc. • Traditional hierarchical algorithms use a similarity or distance matrix to merge or split one cluster at a time 14
- 15. Prof. Pier Luca Lanzi Agglomerative Clustering Algorithm • More popular hierarchical clustering technique • Compute the proximity matrix • Let each data point be a cluster • Repeat §Merge the two closest clusters § Update the proximity matrix • Until only a single cluster remains • Key operation is the computation of the proximity of two clusters • Different approaches to defining the distance between clusters distinguish the different algorithms 15
- 16. Prof. Pier Luca Lanzi Hierarchical Clustering: Time and Space Requirements • O(N2) space since it uses the proximity matrix. §N is the number of points. • O(N3) time in many cases §There are N steps and at each step the size, N2, proximity matrix must be updated and searched §Complexity can be reduced to O(N2 log(N) ) time for some approaches 16
- 17. Prof. Pier Luca Lanzi Efficient Implementation • Compute the distance between all pairs of points [O(N2)] • Insert the pairs and their distances into a priority queue to fine the min in one step [O(N2)] • When two clusters are merged, we remove all entries in the priority queue involving one of these two clusters [O(Nlog N)] • Compute all the distances between the new cluster and the re- maining clusters [O(NlogN)] • Since the last two steps are executed at most N time, the complexity of the whole algorithms is O(N2logN) 17
- 18. Prof. Pier Luca Lanzi Distance Between Clusters
- 19. Prof. Pier Luca Lanzi Initial Configuration • Start with clusters of individual points and the distance matrix ... p1 p2 p3 p4 p9 p10 p11 p12 p1 p3 p5 p4 p2 p1 p2 p3 p4 p5 . . . . . . Distance Matrix 19
- 20. Prof. Pier Luca Lanzi Intermediate Situation • After some merging steps, we have some clusters ... p1 p2 p3 p4 p9 p10 p11 p12 C1 C4 C2 C5 C3 C2C1 C1 C3 C5 C4 C2 C3 C4 C5 Distance Matrix 20
- 21. Prof. Pier Luca Lanzi Intermediate Situation • We want to merge the two closest clusters (C2 and C5) and update the proximity matrix. ... p1 p2 p3 p4 p9 p10 p11 p12 C1 C4 C2 C5 C3 C2C1 C1 C3 C5 C4 C2 C3 C4 C5 Distance Matrix 21
- 22. Prof. Pier Luca Lanzi After Merging • The question is “How do we update the proximity matrix?” ... p1 p2 p3 p4 p9 p10 p11 p12 C1 C4 C2 U C5 C3 ? ? ? ? ? ? ? C2 U C5C1 C1 C3 C4 C2 U C5 C3 C4 Distance Matrix 22
- 23. Prof. Pier Luca Lanzi Similarity?
- 24. Prof. Pier Luca Lanzi Single Linkage or MIN
- 25. Prof. Pier Luca Lanzi Complete Linkage or MAX
- 26. Prof. Pier Luca Lanzi Average or Group Average
- 27. Prof. Pier Luca Lanzi Distance between Centroids ´ ´
- 28. Prof. Pier Luca Lanzi Typical Alternatives to Calculate the Distance Between Clusters • Single link (or MIN) §smallest distance between an element in one cluster and an element in the other, i.e., d(Ci, Cj) = min(ti,p, tj,q) • Complete link (or MAX) §largest distance between an element in one cluster and an element in the other, i.e., d(Ci, Cj) = max(ti,p, tj,q) • Average (or group average) §average distance between an element in one cluster and an element in the other, i.e., d(Ci, Cj) = avg(d(ti,p, tj,q)) • Centroid §distance between the centroids of two clusters, i.e., d(Ci, Cj) = d(μi, μj) where μi and μi are the centroids • … 28
- 29. Prof. Pier Luca Lanzi Example • Suppose we have five items, a, b, c, d, and e. • We wanto to perform hierarchical clustering on five instances following an agglomerative approach • First: we compute the distance or similarity matrix • Dij is the distance between instancce “i” and “j” ÷ ÷ ÷ ÷ ÷ ÷ ø ö ç ç ç ç ç ç è æ = 0003050809 000409010 000506 0002 00 ..... .... ... .. . D 29
- 30. Prof. Pier Luca Lanzi Example • Group the two instances that are closer • In this case, a and b are the closest items (D2,1=2) • Compute again the distance matrix, and start again. • Suppose we apply single-linkage (MIN), we need to compute the distance between the new cluster {1,2} and the others §d(12)3 = min[d13,d23] = d23 = 5.0 §d(12)4 = min[d14,d24] = d24 = 9.0 §d(12)5 = min[d15,d25] = d25 = 8.0 30
- 31. Prof. Pier Luca Lanzi Example • The new distance matrix is, ÷ ÷ ÷ ÷ ÷ ø ö ç ç ç ç ç è æ = 0.00.30.50.8 0.00.40.9 0.00.5 0.0 D 31 • At the end, we obtain the following dendrogram
- 32. Prof. Pier Luca Lanzi Determining the Number of Clusters 32
- 33. Prof. Pier Luca Lanzi hierarchical clustering generates a set of N possible partitions which one should I choose?
- 34. Prof. Pier Luca Lanzi From the previous lecture we know ideally a good cluster should partition points so that … Data points in the same cluster should have a small distance from one another Data points in different clusters should be at a large distance from one another.
- 35. Prof. Pier Luca Lanzi Within/Between Clusters Sum of Squares • Within-cluster sum of squares where μi is the centroid of cluster Ci (in case of Euclidean spaces) • Between-cluster sum of squares where μ is the centroid of the whole dataset 35
- 36. Prof. Pier Luca Lanzi Within/Between Clusters Sum of Squares (for distance function d) • Within-cluster sum of squares where μi is the centroid of cluster Ci (in case of Euclidean spaces) • Between-cluster sum of squares where μ is the centroid of the whole dataset 36
- 37. Prof. Pier Luca Lanzi Evaluation of Hierarchical Clustering using Knee/Elbow Analysis plot the WSS and BSS for every clustering and look for a knee in the plot that show a significant modification in the evaluation metrics
- 38. Prof. Pier Luca Lanzi Run the Python notebook for hierarchical clustering
- 39. Prof. Pier Luca Lanzi Example data generated using the make_blob function of Scikit-Learn
- 40. Prof. Pier Luca Lanzi Dendrogram computed using single linkage.
- 41. Prof. Pier Luca Lanzi BSS and WSS for values of k from 1 until 19.
- 42. Prof. Pier Luca Lanzi Clusters produced for values of k from 2 to 7.
- 43. Prof. Pier Luca Lanzi Clusters produced for values of k from 5 to 10.
- 44. Prof. Pier Luca Lanzi How can we represent clusters?
- 45. Prof. Pier Luca Lanzi Euclidean vs Non-Euclidean Spaces • Euclidean Spaces § We can identify a cluster using for instance its centroid (e.g. computed as the average among all its data points) § Alternatively, we can use its convex hull • Non-Euclidean Spaces § We can define a distance (jaccard, cosine, edit) § We cannot compute a centroid and we can introduce the concept of clustroid • Clustroid § An existing data point that we take as a cluster representative § It can be the point that minimizes the sum of the distances to the other points in the cluster § Or, the one minimizing the maximum distance to another point § Or, the sum of the squares of the distances to the other points in the cluster 45
- 46. Prof. Pier Luca Lanzi Examples using KNIME
- 47. Prof. Pier Luca Lanzi Evaluation of the result from hierarchical clustering with 3 clusters and average linkage against existing labels
- 48. Prof. Pier Luca Lanzi Comparison of hierarchical clustering with 3 clusters and average linkage against k-Means with k=3
- 49. Prof. Pier Luca Lanzi Computing cluster quality from one to 20 clusters using the entropy scorer
- 50. Prof. Pier Luca Lanzi Examples using R
- 51. Prof. Pier Luca Lanzi Hierarchical Clustering in R # init the seed to be able to repeat the experiment set.seed(1234) par(mar=c(0,0,0,0)) # randomly generates the data x<-rnorm(12, mean=rep(1:3,each=4), sd=0.2) y<-rnorm(12, mean=rep(c(1,2,1),each=4), sd=0.2) plot(x,y,pch=19,cex=2,col="blue") # distance matrix d <- data.frame(x,y) dm <- dist(d) # generate the cl <- hclust(dm) plot(cl) # other ways to plot dendrograms # http://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html 51
- 52. Prof. Pier Luca Lanzi Evaluation of Clustering in R library(GMD) ### ### checking the quality of the previous cluster ### # init two vectors that will contain the evaluation # in terms of within and between sum of squares plot_wss = rep(0,12) plot_bss = rep(0,12) # evaluate every clustering for(i in 1:12) { clusters <- cutree(cl,i) eval <- css(dm,clusters); plot_wss[i] <- eval$totwss plot_bss[i] <- eval$totbss } 52
- 53. Prof. Pier Luca Lanzi Evaluation of Clustering in R # plot the results x = 1:12 plot(x, y=plot_bss, main="Between Cluster Sum-of-square", cex=2, pch=18, col="blue", xlab="Number of Clusters", ylab="Evaluation") lines(x, plot_bss, col="blue") par(new=TRUE) plot(x, y=plot_wss, cex=2, pch=19, col="red", ylab="", xlab="") lines(x,plot_wss, col="red"); 53
- 54. Prof. Pier Luca Lanzi Knee/Elbow Analysis of Clustering 54
- 55. Prof. Pier Luca Lanzi Hierarchical Clustering in R – Iris2D library(foreign) iris = read.arff("iris.2D.arff") with(iris, plot(petallength,petalwidth, col="blue", pch=19, cex=2)) dm <- dist(iris[,1:2]) cl <- hclust(iris_dist, method="single") #clustering <- hclust(dist(iris[,1:2],method="manhattan"), method="single") plot(cl) cl_average <- hclust(iris_dist, method="average") plot(clustering) cutree(clustering,2) 55
- 56. Prof. Pier Luca Lanzi Knee/Elbow Analysis of Clustering for iris2D 56
- 57. Prof. Pier Luca Lanzi Knee/Elbow Analysis of Clustering for iris 57
- 58. Prof. Pier Luca Lanzi Summary
- 59. Prof. Pier Luca Lanzi Hierarchical Clustering: Problems and Limitations • Once a decision is made to combine two clusters, it cannot be undone • No objective function is directly minimized • Different schemes have problems with one or more of the following: §Sensitivity to noise and outliers §Difficulty handling different sized clusters and convex shapes §Breaking large clusters • Major weakness of agglomerative clustering methods §They do not scale well: time complexity of at least O(n2), where n is the number of total objects §They can never undo what was done previously 59

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