Big Data: The Science of Patterns. Dr. Lutz Hamel Dept. of Computer Science and Statistics
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1 Big Data: The Science of Patterns Dr. Lutz Hamel Dept. of Computer Science and Statistics
2 The Blessing and the Curse: Lots of Data Outlook Temp Humidity Wind Play Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mold High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mold High Strong Yes Overcast Hot Normal Weak Yes Rain Mold High Strong No 1 petabyte is 2 50 bytes, 1024 terabytes, or a million gigabytes Take Yahoo Inc.'s 2-petabyte, specially built data warehouse, which it uses to analyze the behavior of its half-billion Web visitors per month (2008).
3 The Problem: Data vs. Information Outlook Temp Humidity Wind Play Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mold High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mold High Strong Yes Overcast Hot Normal Weak Yes Rain Mold High Strong No Data Information
4 Information as Patterns From an AI perspective information is represented as patterns patterns summarize large collections of data patterns can be converted into actionable information in Yahoo s case, web behavior patterns can be connected to the kinds of online ads Yahoo might show to its customers. patterns come in all kinds of shapes and forms graphical, rule-based, visual, numeric, etc.
5 Can You find some Patterns here? Outlook Temp Humidity Wind Play Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mold High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mold High Strong Yes Overcast Hot Normal Weak Yes Rain Mold High Strong No Data Information
6 A Tree based Pattern Outlook Temp Humidity Wind Play Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mold High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mold High Strong Yes Overcast Hot Normal Weak Yes Rain Mold High Strong No Data Information
7 Rule based Patterns Best rules found: 1. outlook=overcast 4 ==> play=yes 4 conf:(1) 2. temperature=cool 4 ==> humidity=normal 4 conf:(1) 3. humidity=normal windy=false 4 ==> play=yes 4 conf:(1) 4. outlook=sunny play=no 3 ==> humidity=high 3 conf:(1) 5. outlook=sunny humidity=high 3 ==> play=no 3 conf:(1) 6. outlook=rainy play=yes 3 ==> windy=false 3 conf:(1) 7. outlook=rainy windy=false 3 ==> play=yes 3 conf:(1) 8. temperature=cool play=yes 3 ==> humidity=normal 3 conf:(1) 9. outlook=sunny temperature=hot 2 ==> humidity=high 2 conf:(1) 10. temperature=hot play=no 2 ==> outlook=sunny 2 conf:(1) These are called association rules.
8 Visual Pattern Bacterium b-cereus on different agars Self-Organizing Map Mannitol Chocolate Blood Nutrient Blood You are what you eat!
9 Numeric Patterns ANNs learn numeric patterns on the weighted connections of their neurons.
10 Decision Tree Learning Supervised Learning we have a key concept (target attribute) that we want to learn, e.g. when to play tennis. The key idea is that the attributes and their values should be used to sort the data instances in such a way that target attribute is a non-random as possible its entropy as close to 0 as possible.
11 Entropy S is a sample of training examples p + is the proportion of positive examples in S p - is the proportion of negative examples in S Entropy measures the impurity (randomness) of S p + Entropy(S) - p + log 2 p + - p - log 2 p -
12 Decision Tree Learning Recursive Algorithm Main loop: 1 Let attribute A be the attribute that minimizes the average entropy at the current node 2 For each attribute value of A, create new decendents of current node 3 Sort training examples to decendents 4 If training examples are perfectly sorted (entropy=0), then STOP, else iterate over decendents.
13 How does that exactly work? Outlook Temp Humidity Wind Play Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mold High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mold High Strong Yes Overcast Hot Normal Weak Yes Rain Mold High Strong No Data Information
14 Decision Tree Learning
15 Decision Tree Learning
16 Decision Tree Learning
17 Decision Tree Learning
18 The Limits of Big Data There are some domains that are infinite, that is, there are an infinite number of distinct observations one could collect. That implies that even the biggest data warehouse will not be able to store all the observations. That means we are building patterns from only partial information a problem for inductive reasoning.
19 The Limits of Big Data Karl Popper ( ) was a philosopher of science and first put forward the problem of inductive reasoning as the black swan problem. With a limited sample on swans you will most likely conclude that all swans are white. But it turns out there are black swans in Australia. Paraphrasing this: our patterns are only as good as the data which generated them.
20 The Limits of Big Data If your data warehouse only captures D of the overall data universe X, then your pattern: all swans are white will not be correct.
21 Weka Demo Weka is a data mining tool freely available on the web. It is written in Java and should probably not be used for big data projects but it is a great way to experiment with tools and techniques important to big data projects. repository.seasr.org/datasets/uci/arff/mushroom.arff
22 Resources and References Tom Mitchell -- Machine Learning, McGraw Hill, 1997 Karl Popper -- The Logic of Scientific Discovery, 1934 (as Logik der Forschung, English translation 1959), ISBN Weka Data Mining -- Yahoo! data warehouse -- Decision tree learning -- list=plbv09bd7ez_4tembw7vla19p3tdqh6fyo Classifying Bacteria using SOM -- Bayesian Probability Approach to Feature Significance for Infrared Spectra of Bacteria, Lutz Hamel, Chris W. Brown, Applied Spectroscopy, Volume 66, Number 1, 2012.
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