# Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov

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1 Data Clustering Dec 2nd, 2013 Kyrylo Bessonov

2 Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms k-means Hierarchical Main aims of clustering Classification Grouping of data R functions for clustering k-means Principal Component Analysis (PCA)

3 Clustering grouping of data e.g. cluster #2 Clustering refers to the process of data division into groups Hopefully groups are informative Natural concept to humans (i.e. classification) e.g. cluster #1 Source: Cluster is a group of objects or data points that are similar between themselves and dissimilar to other objects assigned to other clusters

4 Usefulness of data clustering Uses of clustering To discover new categories in data To find hidden structure in data Simplify data analysis by working on subset of data working with clusters instead of individual observations Can be applied to many contexts (same techniques): Marketing: grouping of customers with similar behavior Biology: classification of patients based on clinical history Libraries: order books based into categories(e.g. science fiction, detective, etc.)

5 Clustering algorithm requirements Be able to deal with different types of data E.g. continuous, categorical, rank-based Being able to discover clusters of irregular shape (not only circular) Being able to deal with high-dimensionality Minimal input parameters (if any) Interpretability and usability Reasonably fast (computationally efficient)

6 Main types of clustering Exclusive One data point should belong only to 1 cluster Overlapping Given data point can belong to more than 1 cluster Hierarchical Progressively merge small clusters into bigger ones while building a hierarchical tree Probabilistic Uses probabilistic model to determine on how given data point was generated

7 Exclusive clustering One data point can only belong to only ONE cluster Algorithms: K-means, SVNs, EM (expectation maximization) No overlapping is observed between clusters K-means is the most popular

8 Similarity measures (1) How one decides non-visually if given data point belongs to given cluster? Similarity measure s(x i,x k ) assesses similarity between obeservations large if x i,x k are similar Dis-similarity measure d(x i,x k ) assesses dis-similarity Large if x i,x k are dis-similar Could be thought as a distance between data points

9 Similarity measures (2) Distance measures: Euclidean distance (in 2D similar to distance) Manhattan (city block) distance Good clustering Bad clustering

10 Similarity measures (3) Cosine similarity Smaller the angle, the greater is similarity Used in text mining Correlation coefficient Similar to cosine similarity Not susceptible to scale/absolute values Looks at shape

11 Misclassification errors All clustering algorithms suffer from errors In classification context: our aim is to assign new unknown samples to known classes (e.g. clusters labeled with class) Misclassification happens with sample X belonging to clusters A is wrongly assigned to cluster B In this case clustering algorithm produces wrong class assignment to the sample X Could be estimated using %accuracy, %precision, ROC curves, other performance measures

12 Misclassification measures Accuracy Precision TP TN TP TN FP FN TP TP FP Recall/True Positive Rate (TPR) TP TP FN False Discovery Rate (FDR) FP FP TP TP = true positive predicted result that matches the golden standard TN= true negative predicted result that matched the golden standard FN = the false negative predicted result that is actually positive according to gold standard FP= the false positive predicted result that is actually negative according to gold

13 Unsupervised clustering features Data has no labels or classified yet Algorithm does not take into account information about sample classes or labels Parametric approach Estimate parameters about data distribution Hard to implement for complex datasets Non-parametric Minimum input parameters required No information about data is needed Data grouped into clusters

14 Unsupervised clustering questions Are there clusters present in the data? Does the obtained clusters via method X are in agreement with the prior knowledge? Do the identified clusters fit the data well based on specific parameter(e.g. error function is minimized)? If true sample labels are known (but not used during clustering), how well the obtained clusters classify samples? (external validation, a posteriori)

15 Supervised clustering Sample labels used in clustering Used to look for outliers in a given class Identify mislabeled samples Maximize class purity Algorithms Supervised Partitioning Around Medoids (SPAM) Supervised Clustering using Evolutionary Computing (SCEC)

16 Unsupervised VS Supervised Unsupervised clustering created impure cluster A Supervised clustering made cluster A pure (classes are known and used during clustering)

17 K-means Simplest unsupervised clustering algorithm Requires specification of expected number of clusters (i.e. k = # of clusters) Similarity is calculated based on Euclidian distance k=3

18 K-means algorithm Given dataset X={x 1,.x n } assign to clusters c={c 1,,c k } 1. Place centroids u={u 1,..,u k } as far apart as possible. If not a 1 st iteration, recalculate centroids of the clusters c 2. Take points in the dataset and associate them to centroids u populating clusters c 3. Repeat steps 1-3 until no centroids do not move and acceptable error function threshold is met

19 Hierarchical clustering Divides or aggregates data recursively into groups Agglomerative Bottom-up approach. Start from small clusters and start aggregation process to create larger clusters Divisive From one cluster representing whole data start dividing it into small parts Data separation process is based on nested clusters Smaller clusters within other bigger clusters Two nested clusters green and blue with 2 smaller clusters each

20 Hierarchical clustering Agglomerative: 1. Start by assigning each data point to a cluster c i{1..n} so that you have n clusters each containing just one item 2. Find the pair of clusters c i and c j (having the smallest distance/largest similarity between them) and merge them into a single cluster c k =c i c j 3. Eliminate selected clusters c i and c j from the list of clusters c 4. Compute and update distances (similarities) between the new cluster and each of the old clusters 5. Repeat steps 2-4 until the total number of clusters n=1 (one big cluster) Hierarchical tree

21 Hierarchical tree Tree levels cut tree horizontally top levels larger clusters bottom levels smaller clusters the very bottom level of the tree contains clusters with class label assigned smallest clusters Branches of the tree represent distances between clusters Source: Daqing Mao, Yi Luo, Jinghai Zhang and Jun Zhu A NEW STRATEGY OF COOPERATIVITY OF BICLUSTERING AND HIERARCHICAL CLUSTERING: A CASE OF ANALYZING YEAST GENOMIC MICROARRAY DATASETS

22 Hierarchical clustering - distance Cluster distance measured Minimal distance (single linkage, nearest neighbor) Encourages growth of elongated clusters Sensitive to noise Maximum distance (complete linkage) Encourages compact clusters Does not work well on elongated clusters Average distance Mean distance More robust to noise, more stable Cheaper to compute than average distance

23 Principal component analysis (PCA) Non-parametric method of extracting patterns in data from complex/high-dimentional datasets Used for data compression and simplification Extraction of relevant data Reduction of data dimentionality Comes from linear algebra Relies on variance present in data Not a clustering technique, but used together with clustering such as k-means

24 Principal component analysis (PCA) Assumes linearity in data Hidden factors in data have an additive nature Usually a good approximation to take Designed to analyze variance in data via Covariance matrix Correlation matrix Data could be transformed to lower dimensions via eigenvectors (principal components) capturing the largest variance in input data

25 PCA algorithm in nutshell Relies on manipulation of matrices via linear algebra 1. Subtract mean from each variable vector (e.g. gene or individual, etc.) centers data 2. Calculate co-variance matrix 3. Calculate the eigenvectors and eigenvalues of the covariance matrix 4. Choose principal components (PCs) the eigenvector with the highest eigenvalue is the principle component of the data set eigenvectors are perpendicular to each other 5. Transpose data along selected principal components

26 PCA illustration example (1) 1) Subtract means Xbar and Ybar from the data x y X 1.81 Y 1.91 x y

27 PCA illustration example (2) 2) Calculate co-variance of X and Y vectors: cov() x y x y ) Calculate eigenvalue and eigenvector from the covariance matrix: eigen(covariance matrix) x x y eigenvalues ) Choose eigenvectors (PCs) with highest eigenvalues In these case the 1 st eigenvector could be selected (PC1) y PC1 PC2

28 PCA illustration example (3) 5) Transpose data along selected PCs As expected PC1 captures most data variance To transpose multiply selected eigenvectors by initial data Transposed data = eigenvectors x mean-subtracted data

29 R implementation of PCA R contains several functions to perform pca princomp(): stats library prcomp(): stats library PCA (): FactoMineR library

30 prcomp() function > pca = prcomp(data, retx=t) > pca Standard deviations: [1] Rotation: PC1 PC2 x y Co-variance matrix with PCs along columns

31 prcomp() function Data is rotated or superimposed onto the selected 2 PCs acting as news axis New coordinates for x and y vectors along PC1 and PC2 pca\$x PC1 PC2 [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,]

32 prcomp() function Obtaining information on variance explained by each PC > summary(pca) Importance of components: Comp.1 Comp.2 Standard deviation Proportion of Variance Cumulative Proportion

33 princomp() function in R > pca = princomp(data, retx=t) > pca Call: princomp(x = data, retx = T) Standard deviations: Comp.1 Comp variables and 10 observations.

34 princomp() function in R > attributes(pca) \$names [1] "sdev" "loadings" "center" "scale" "n.obs" "scores" "call" \$class [1] "princomp" > pca\$scores Comp.1 Comp.2 [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,] Rotated along PC1 and PC2 values of mean subtracted vectors X and Y

36 Data Mining field Data Mining (DM) is a field that uses other techniques to analyze data and to find patterns in data The techniques used are often do not have solid statistical basis DM based methods can process huge amounts of data Classification Rules, Association Rules, Neural Networks, Supporter Vector Machines

37 Final remarks Making sense of data is very important task for XXI century Easy to generate data Hard to make any sense of collected data The data clustering and data complexity reduction techniques could be applied in any discipline (Biology, Physics, Economics) No method is perfect Active research in fields related to Big Data analysis Data Mining, Statistics, Machine Learning

38 References apters/chapter004/chapter.html

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