Clustering Tutorial

1. Clustering Tutorial Elias Raftopoulos HY539 29/3/06 Prof. Maria Papadopouli

2. Roadmap • Math Reminder • Principle Components Analysis • Clustering • ANOVA

3. Standard Deviation • Statistics – analyzing data sets in terms of the relationships between the individual points • Standard Deviation is a measure of the spread of the data • Calculation: average distance from the mean of the data

4. Variance • Another measure of the spread of the data in a data set • Calculation: Var( X ) = E(( x – μ )^2) • Why have both variance and SD to calculate the spread of data? • Variance is claimed to be the original statistical measure of spread of data. However it’s unit would be expressed as a square e.g. cm^2, which is unrealistic to express heights or other measures. Hence SD as the square root of variance was born.

5. Covariance • Variance – measure of the deviation from the mean for points in one dimension e.g. heights • Covariance as a measure of how much each of the dimensions vary from the mean with respect to each other. • Covariance is measured between 2 dimensions to see if there is a relationship between the 2 dimensions e.g. number of hours studied & marks obtained • The covariance between one dimension and itself is the variance

6. Covariance Matrix • Representing Covariance between dimensions as a matrix e.g. for 3 dimensions: cov(x,x) cov(x,y) cov(x,z) C = cov(y,x) cov(y,y) cov(y,z) cov(z,x) cov(z,y) cov(z,z) • Diagonal is the variances of x, y and z • cov(x,y) = cov(y,x) hence matrix is symmetrical about the diagonal • N-dimensional data will result in nxn covariance matrix

7. Covariance • Exact value is not as important as it’s sign. • A positive value of covariance indicates both dimensions increase or decrease together e.g. as the number of hours studied increases, the marks in that subject increase. • A negative value indicates while one increases the other decreases, or vice-versa e.g. active social life at RIT vs performance in CS dept. • If covariance is zero: the two dimensions are independent of each other e.g. heights of students vs the marks obtained in a subject

8. Transformation matrices x = = x 4 • Consider: 2 3 3 12 3 2 1 2 8 2 • Square transformation matrix transforms (3,2) from its original location. Now if we were to take a multiple of (3,2) 3 6 2 4 2 3 6 24 6 2 1 4 16 4 2 x = x = = x 4

9. Transformation matrices • Scale vector (3,2) by a value 2 to get (6,4) • Multiply by the square transformation matrix • We see the result is still a multiple of 4. • WHY? • A vector consists of both length and direction. Scaling a vector only changes its length and not its direction. This is an important observation in the transformation of matrices leading to formation of eigenvectors and eigenvalues. • Irrespective of how much we scale (3,2) by, the solution is always a multiple of 4.

10. eigenvalue problem • The eigenvalue problem is any problem having the following form: A . v = λ . v • A: n x n matrix • v: n x 1 non-zero vector • λ: scalar • Any value of λ for which this equation has a solution is called the eigenvalue of A and vector v which corresponds to this value is called the eigenvector of A.

11. eigenvalue problem x = = x 4 2 3 3 12 3 2 1 2 8 2 A . v = λ.v Therefore, (3,2) is an eigenvector of the square matrix A and 4 is an eigenvalue of A Given matrix A, how can we calculate the eigenvector and eigenvalues for A?

12. Calculating eigenvectors & eigenvalues Given A . v = λ.v A . v - λ.I.v = 0 (A - λ.I ).v = 0 Finding the roots of |A - λ.I| will give the eigenvalues and for each of these eigenvalues there will be an eigenvector Example …

13. Calculating eigenvectors & eigenvalues If A = 0 1 -2 -3 Then |A - λ.I| = 0 1 λ 0 = 0 -2 -3 0 λ -λ 1 = λ2 + 3λ + 2 = 0 -2 -3-λ This gives us 2 eigenvalues: λ1 = -1 and λ2 = -2

14. Properties of eigenvectors and eigenvalues • Note that Irrespective of how much we scale (3,2) by, the solution is always a multiple of 4. • Eigenvectors can only be found for square matrices and not every square matrix has eigenvectors. • Given an n x n matrix, we can find n eigenvectors

15. Roadmap • Principle Components Analysis • Clustering • ANOVA

16. PCA • principal components analysis (PCA) is a technique that can be used to simplify a dataset • It is a linear transformation that chooses a new coordinate system for the data set such that • greatest variance by any projection of the data set comes to lie on the first axis (then called the first principal component), • the second greatest variance on the second axis, and so on. • PCA can be used for reducing dimensionality by eliminating the later principal components.

17. PCA • By finding the eigenvalues and eigenvectors of the covariance matrix, we find that the eigenvectors with the largest eigenvalues correspond to the dimensions that have the strongest correlation in the dataset. • This is the principal component. • PCA is a useful statistical technique that has found application in: • fields such as face recognition and image compression • finding patterns in data of high dimension

18. PCA process –STEP 1 • Subtract the mean from each of the data dimensions. All the x values have x subtracted and y values have y subtracted from them. This produces a data set whose mean is zero. Subtracting the mean makes variance and covariance calculation easier by simplifying their equations. The variance and co-variance values are not affected by the mean value.

19. PCA process –STEP 1 ZERO MEAN DATA: x y .69 .49 -1.31 -1.21 .39 .99 .09 .29 1.29 1.09 .49 .79 .19 -.31 -.81 -.81 -.31 -.31 -.71 -1.01 DATA: x y 2.5 2.4 0.5 0.7 2.2 2.9 1.9 2.2 3.1 3.0 2.3 2.7 2 1.6 1 1.1 1.5 1.6 1.1 0.9

20. PCA process –STEP 1

21. PCA process –STEP 2 • Calculate the covariance matrix cov = .616555556 .615444444 .615444444 .716555556 • since the non-diagonal elements in this covariance matrix are positive, we should expect that both the x and y variable increase together.

22. PCA process –STEP 3 • Calculate the eigenvectors and eigenvalues of the covariance matrix eigenvalues = .0490833989 1.28402771 eigenvectors = -.735178656 -.677873399 .677873399 -.735178656

23. PCA process –STEP 3 • eigenvectors are plotted as diagonal dotted lines on the plot. • Note they are perpendicular to each other. • Note one of the eigenvectors goes through the middle of the points, like drawing a line of best fit. • The second eigenvector gives us the other, less important, pattern in the data, that all the points follow the main line, but are off to the side of the main line by some amount.

24. PCA process –STEP 4 • Reduce dimensionality and form feature vector the eigenvector with the highest eigenvalue is the principle component of the data set. • In our example, the eigenvector with the larges eigenvalue was the one that pointed down the middle of the data. • Once eigenvectors are found from the covariance matrix, the next step is to order them by eigenvalue, highest to lowest. This gives you the components in order of significance.

25. PCA process –STEP 4 • Now, if you like, you can decide to ignore the components of lesser significance • You do lose some information, but if the eigenvalues are small, you don’t lose much • n dimensions in your data • calculate n eigenvectors and eigenvalues • choose only the first p eigenvectors • final data set has only p dimensions.

26. PCA process –STEP 4 • Feature Vector FeatureVector = (eig1 eig2 eig3 … eign) We can either form a feature vector with both of the eigenvectors: -.677873399 -.735178656 -.735178656 .677873399 or, we can choose to leave out the smaller, less significant component and only have a single column: - .677873399 - .735178656

27. PCA process –STEP 5 • Deriving the new data FinalData = RowFeatureVector x RowZeroMeanData • RowFeatureVector is the matrix with the eigenvectors in the columns transposed so that the eigenvectors are now in the rows, with the most significant eigenvector at the top • RowZeroMeanData is the mean-adjusted data transposed, ie. the data items are in each column, with each row holding a separate dimension.

28. PCA process –STEP 5 S R = U VT factors variables factors variables sig. significant noise noise noise significant factors factors samples samples

29. PCA process –STEP 5 • FinalData is the final data set, with data items in columns, and dimensions along rows. • What will this give us? • It will give us the original data solely in terms of the vectors we chose. • We have changed our data from being in terms of the axes x and y , and now they are in terms of our 2 eigenvectors.

30. PCA process –STEP 5 FinalData transpose: dimensions along columns x y -.827970186 -.175115307 1.77758033 .142857227 -.992197494 .384374989 -.274210416 .130417207 -1.67580142 -.209498461 -.912949103 .175282444 .0991094375 -.349824698 1.14457216 .0464172582 .438046137 .0177646297 1.22382056 -.162675287

31. PCA process –STEP 5

32. Reconstruction of original Data • If we reduced the dimensionality, obviously, when reconstructing the data we would lose those dimensions we chose to discard. In our example let us assume that we considered only the x dimension…

33. Reconstruction of original Data x -.827970186 1.77758033 -.992197494 -.274210416 -1.67580142 -.912949103 .0991094375 1.14457216 .438046137 1.22382056

34. Roadmap • Principle Components Analysis • Clustering • ANOVA

35. What is Cluster Analysis? • Cluster: a collection of data objects • Similar to the objects in the same cluster (Intraclass similarity) • Dissimilar to the objects in other clusters (Interclass dissimilarity) • Cluster analysis • Statistical method for grouping a set of data objects into clusters • A good clustering method produces high quality clusters with high intraclass similarity and low interclass similarity • Clustering is unsupervised classification • Can be a stand-alone tool or as a preprocessing step for other algorithms

36. Group objects according to their similarity Cluster: a set of objects that are similar to each other and separated from the other objects. Example: green/ red data points were generated from two different normal distributions

37. Clustering data object expression data matrix • Experiments/samples are given as the row and column vectors of an expression data matrix • Clustering may be applied either to objects experiments (regarded as vectors in Ro or Rn). n experiments o objects

38. Pattern matrix  Proximity matrix • Pattern matrix (nxp) • p=attributes • n=# of objects • Proximity matrix (nxn) • d(i,j)=difference/ dissimilarity between i and j

39. Proximity matrix • Clustering methods require that a index of proximity, or alikeness, or affinity or association be established between pairs of patterns • A proximity index is either a similarity or a dissimilarity • The crucial problem in identifying clusters in data is to specify what proximity is and how to measure it

40. Proximity indices • A proximity index between the ith and kth patterns is denoted d(i,k) and must satisfy the following three properties: 1. (a) for a dissimilarity: d(i,i) = 0, all i (b) for a similarity: d(i,i) ≥ max d(i,k), all I 2. d(i,k) = d(k,i), all (i,k) 3. d(i,k) ≥ 0, all (i,k)

41. Different proximity measures • r = 2(Euclidean distance) [42 + 22]1/2 = 4.472 • r = 1(Manhattan distance) 4 + 2 = 6 • r → ∞ (“sup” distance) max{4,2} = 4

42. K-Means Clustering • The meaning of ‘K-means’ • Why it is called ‘K-means’ clustering: K points are used to represent the clustering result; each point corresponds to the centre (mean) of a cluster • Each point is assigned to the cluster with the closest center point • The number K, must be specified • Basic algorithm

43. The K-Means Clustering Method • Given k, the k-means algorithm is implemented in 4 steps: • Partition objects into k non-empty subsets • Arbitrarily choose k points as initial centers • Assign each object to the cluster with the nearest seed point (center) • Calculate the mean of the cluster and update the seed point • Go back to Step 3, stop when no more new assignment

44. The K-Means Clustering Method (cntd) • The basic step of k-means clustering is simple: • Iterate until stable (= no object move group): • Determine the centroid coordinate • Determine the distance of each object to the centroids • Group the object based on minimum distance

45. The K-Means Clustering Method (cntd)

46. 10 9 8 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 8 9 10 The K-Means Clustering Results • Example 10 9 8 7 6 5 Update the cluster means Assign each objects to most similar center 4 3 2 1 0 0 1 2 3 4 5 6 7 8 9 10 reassign reassign K=2 Arbitrarily choose K object as initial cluster center Update the cluster means

47. Weaknesses of the K-Means Method • Unable to handle noisy data and outliers • Very large or very small values could skew the mean • Not suitable to discover clusters with non-convex shapes

48. Hierarchical Clustering • Start with every data point in a separate cluster • Keep merging the most similar pairs of data points/clusters until we have one big cluster left • This is called a bottom-up or agglomerative method

49. Hierarchical Clustering (cont.) • This produces a binary tree or dendrogram • The final cluster is the root and each data item is a leaf • The height of the bars indicate how close the items are

50. Hierarchical Clustering Demo