Data Mining. Jeff M. Phillips. January 9, 2013
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1 Data Mining Jeff M. Phillips January 9, 2013
2 Data Mining What is Data Mining? Finding structure in data? Machine learning on large data? Unsupervised learning? Large scale computational statistics?
3 Data Mining What is Data Mining? Finding structure in data? Machine learning on large data? Unsupervised learning? Large scale computational statistics? What you can recover from data and what you cannot recover. Algorithms for how to recover it efficiently.
4 Data Mining What is Data Mining? Finding structure in data? Machine learning on large data? Unsupervised learning? Large scale computational statistics? What you can recover from data and what you cannot recover. Algorithms for how to recover it efficiently. How to think about data analytics.
5 Modeling versus Efficiency Two Intertwined Objectives: Model Data Correctly Process Data Efficiently Statistics Machine Learning Data Mining Algorithms Modeling Efficiency
6 Outline Statistical Principals: 1. Understanding random effects Data and Distances: 2. Similarity (find duplicates and similar items) 3. Clustering (aggregate close items) Structure in Data: 3. Clustering (aggregate close items) 4. Regression (patterns in data) 5. Anomaly Detection (outliers in data) Controlling for Noise and Uncertainty: 5. Anomaly Detection (outliers in data) 6. Link Analysis (prominent structure in large graphs) 7. Summaries (concise representation)
7 Statistical Principals What happens as data is generated with replacement {IP addresses, words in dictionary, edges in graph, hash table} When do items collide? When do you see all items? When is the distribution almost uniform?
8 Statistical Principals What happens as data is generated with replacement {IP addresses, words in dictionary, edges in graph, hash table} When do items collide? When do you see all items? When is the distribution almost uniform?
9 Statistical Principals What happens as data is generated with replacement {IP addresses, words in dictionary, edges in graph, hash table} When do items collide? When do you see all items? When is the distribution almost uniform?
10 Statistical Principals What happens as data is generated with replacement {IP addresses, words in dictionary, edges in graph, hash table} When do items collide? When do you see all items? When is the distribution almost uniform?
11 Raw Data to Abstract Representations How to measure similarity between data? Key idea: data point a quick brown fox jumped joe bob sue age income height K 45K 38K
12 Similarity Given a large set of data P. Given new point q, is q in P? Given a large set of data P. Given new point q, what is closest point in P to q? q P
13 Clustering How to find groups of similar data. do we need a representative? can groups overlap? what is structure of data/distance?
14 Clustering How to find groups of similar data. do we need a representative? can groups overlap? what is structure of data/distance? Hierarchical clustering : When to combine groups? k-means clustering : k-median, k-center, k-means++ Graph clustering : modularity, spectral
15 Regression Consider a data set P R d, where d is BIG! Want to find representation of P in some R k µ(p) Q R k so p i p j q i q j Q R k should capture most data in P
16 Regression Consider a data set P R d, where d is BIG! Want to find representation of P in some R k µ(p) Q R k so p i p j q i q j Q R k should capture most data in P L 2 Regression + PCA : Common easy approach Multidimensional Scaling : Fits in R k with k small Random Projections : Faster and easier (different bounds) L 1 Regression : Better, Orthogonal Matching Pursuit Special Topic : Compressed Sensing
17 Anomaly Detection What to do when data is noisy? Identify it : Find and remove outliers Model it : It may be real, affect answer Exploit it : Differential privacy (special topic)
18 Link Analysis How does Google Search work? Converts webpage links into directed graph. Markov Chains : Models movement in a graph PageRank : How to convert graph into important nodes MapReduce : How to scale up PageRank Communities : Other important nodes in graphs
19 Summaries Reducing massive data to small space. Want to retain as much as possible (not specific structure) error guarantees OnePass Sampling : Reservoir Sampling Density Approximation : Quantiles MinCount Hash : Sketching data, abstract features Spanners : graph approximations [...] :... on request... memory length m CPU word 2 [n] u ω(k, u) ω(p, u)
20 Themes What are course goals? Intuition for data analytics How to model data (convert to abstract data types) How to process data efficiently (balance models with algorithms)
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22 Data Group Data Group Meeting noonish in Graphics Annex
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