# Machine Learning and Data Mining. Clustering. (adapted from) Prof. Alexander Ihler

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1 Machine Learning and Data Mining Clustering (adapted from) Prof. Alexander Ihler

2 Unsupervised learning Supervised learning Predict target value ( y ) given features ( x ) Unsupervised learning Understand patterns of data (just x ) Useful for many reasons Data mining ( explain ) Missing data values ( impute ) Representation (feature generation or selection) One example: clustering

3 Clustering and Data Compression Clustering is related to vector quantization Dictionary of vectors (the cluster centers) Each original value represented using a dictionary index Each center claims a nearby region (Voronoi region)

4 Hierarchical Agglomerative Clustering Another simple clustering algorithm Initially, every datum is a cluster Define a distance between clusters (return to this) Initialize: every example is a cluster Iterate: Compute distances between all clusters (store for efficiency) Merge two closest clusters Save both clustering and sequence of cluster operations Dendrogram

5 Iteration 1

6 Iteration 2

7 Iteration 3 Builds up a sequence of clusters ( hierarchical ) Algorithm complexity O(N 2 ) (Why?) In matlab: linkage function (stats toolbox)

8 Dendrogram

9 Cluster Distances produces minimal spanning tree. avoids elongated clusters.

10 Example: microarray expression Measure gene expression Various experimental conditions Cancer, normal Time Subjects Explore similarities What genes change together? What conditions are similar? Cluster on both genes and conditions

11 K-Means Clustering A simple clustering algorithm Iterate between Updating the assignment of data to clusters Updating the cluster s summarization Suppose we have K clusters, c=1..k Represent clusters by locations ¹ c Example i has features x i Represent assignment of i th example as z i in 1..K Iterate until convergence: For each datum, find the closest cluster Set each cluster to the mean of all assigned data:

12 Choosing the number of clusters With cost function what is the optimal value of k? (can increasing k ever increase the cost?) This is a model complexity issue Much like choosing lots of features they only (seem to) help But we want our clustering to generalize to new data One solution is to penalize for complexity Bayesian information criterion (BIC) Add (# parameters) * log(n) to the cost Now more clusters can increase cost, if they don t help enough

13 Choosing the number of clusters (2) The Cattell scree test: Dissimilarity Number of Clusters Scree is a loose accumulation of broken rock at the base of a cliff or mountain.

14 Mixtures of Gaussians K-means algorithm Assigned each example to exactly one cluster What if clusters are overlapping? Hard to tell which cluster is right Maybe we should try to remain uncertain Used Euclidean distance What if cluster has a non-circular shape? Gaussian mixture models Clusters modeled as multivariate Gaussians Not just by their mean EM algorithm: assign data to cluster with some probability

15 Multivariate Gaussian models 5 4 Maximum Likelihood estimates We ll model each cluster using one of these Gaussian bells

16 EM Algorithm: E-step Start with parameters describing each cluster Mean μ c, Covariance Σ c, size π c E-step ( Expectation ) For each datum (example) x_i, Compute r_{ic}, the probability that it belongs to cluster c Compute its probability under model c Normalize to sum to one (over clusters c) If x_i is very likely under the c th Gaussian, it gets high weight Denominator just makes r s sum to one

17 EM Algorithm: M-step Start with assignment probabilities r ic Update parameters: mean μ c, Covariance Σ c, size π c M-step ( Maximization ) For each cluster (Gaussian) x_c, Update its parameters using the (weighted) data points Total responsibility allocated to cluster c Fraction of total assigned to cluster c Weighted mean of assigned data Weighted covariance of assigned data (use new weighted means here)

18 Expectation-Maximization Each step increases the log-likelihood of our model (we won t derive this, though) Iterate until convergence Convergence guaranteed another ascent method What should we do If we want to choose a single cluster for an answer? With new data we didn t see during training?

19 4.4 ANEMIA PATIENTS AND CONTROLS Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

20 4.4 EM ITERATION 1 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

21 4.4 EM ITERATION 3 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

22 4.4 EM ITERATION 5 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

23 4.4 EM ITERATION 10 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

24 4.4 EM ITERATION 15 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

25 4.4 EM ITERATION 25 Red Blood Cell Hemoglobin Concentration From P. Smyth ICML Red Blood Cell Volume

26 490 LOG-LIKELIHOOD AS A FUNCTION OF EM ITERATIONS Log-Likelihood From P. Smyth ICML EM Iteration

27 Summary Clustering algorithms Agglomerative clustering K-means Expectation-Maximization Open questions for each application What does it mean to be close or similar? Depends on your particular problem Local versus global notions of similarity Former is easy, but we usually want the latter Is it better to understand the data itself (unsupervised learning), to focus just on the final task (supervised learning), or both?

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