Machine Learning - Clustering. CS102 Fall 2017
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1 Machine Learning - Fall 2017
2 Big Data Tools and Techniques Basic Data Manipulation and Analysis Performing well-defined computations or asking well-defined questions ( queries ) Data Mining Looking for patterns in data Machine Learning Using data to make inferences or predictions Data Visualization Graphical depiction of data Data Collection and Preparation
3 Machine Learning Using data to make inferences or predictions Supervised machine learning Set of labeled examples to learn from: training data Develop model from training data Use model to make inferences about new data Unsupervised machine learning Unlabeled data, look for patterns or structure (similar to data mining)
4 Like classification, data items consist of values for a set of features (numeric or categorical) Medical patients Feature values: age, gender, symptom1-severity, symptom2-severity, test-result1, test-result2 Web pages Feature values: URL domain, length, #images, heading 1, heading 2,, heading n Products Feature values: category, name, size, weight, price
5 Like classification, data items consist of values for a set of features (numeric or categorical) Medical patients Unlike classification, Feature values: age, gender, there symptom1-severity, is no label symptom2-severity, test-result1, test-result2 Web pages Feature values: URL domain, length, #images, heading 1, heading 2,, heading n Products Feature values: category, name, size, weight, price
6 Like K-nearest neighbors, for any pair of data items i 1 and i 2, from their feature values can compute distance function: distance(i 1,i 2 ) Example: Features - gender, profession, age, income, postal-code person 1 = (male, teacher, 47, $25K, 94305) person 2 = (female, teacher, 43, $28K, 94309) distance(person 1, person 2 ) distance() can be defined as inverse of similarity()
7 GOAL: Given a set of data items, partition them into groups (= clusters) so that items within groups are close to each other based on distance function Ø Sometimes number of clusters is pre-specified Ø Typically clusters need not be same size
8 Some Uses for Classification! Assign labels to clusters New data items get the label of their cluster Identify similar items For substitutes or recommendations For de-duplication Anomaly (outlier) detection Items that are far from any cluster
9 K-Means Reminder: for any pair of data items i 1 and i 2 have distance(i 1,i 2 ) For a group of items, the mean value (centroid) of the group is the item i (in the group or not) that minimizes the sum of distance(i,i ) for all i in the group
10 K-Means For a group of items, the mean value (centroid) of the group is the item i (in the group or not) that minimizes the sum of distance(i,i ) for all i in the group Error for each item: distance d from the mean for its group; squared error is d 2 Error for the entire clustering: sum of squared errors (SSE) Remind you of anything?
11 K-Means Given set of data items and desired number of clusters k, K-means groups the items into k clusters minimizing the SSE Extremely difficult to compute efficiently Ø In fact, impossible Most algorithms compute an approximate solution (might not be absolute lowest SSE)
12 European Cities By geographic distance, then by temperature
13 European Cities Distance = actual distance, k = 5
14 European Cities Distance = actual distance, k = 8, with cluster means
15 European Cities Distance = actual distance, k = 2, with cluster means
16 European Cities Distance = actual distance, k = 30
17 European Cities Distance = temperature, k = 5
18 European Cities Distance = temperature, k = 8, with means
19 European Cities Distance = temperature, k = 2
20 European Cities Distance = temperature, k = 3
21 European Cities Distance = temperature, k = 30
22 Some Uses for Classification Assign labels to clusters New data items get the label of their cluster Identify similar items For substitutes or recommendations For de-duplication Anomaly (outlier) detection Items that are far from any cluster
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