DATA MINING and VISUALIZATION

1. DATA MINING and VISUALIZATION Instructor: Dr. Matthew Iklé, Adams State University Remote Instructor: Dr. Hong Liu, Embry-Riddle Aeronautical University Fall 2014

2. COURSE INFORMATION • Course Website: datamined.wordpress.com • Instructor email: moikle@adams.edu • Instructor cell phone: +1 719-588-4487 • Instructor office hours: MWF 10-11 and TR 8:30-9:30 and by appointment (Mountain time) • Required text: Tan, Steinbach, Kumar, Introduction to Data Mining, ISBN: 0-321-32136-7, Pearson Education, 2006. • Recommended text: Witten, Eibe, Hall, Data Mining, Practical Machine Learning Tools and Techniques, ISBN: 978-0-12-374856-0, Elsevier, 2011.

3. COURSE REQUIREMENTS • Minimal prerequisites • Modest background in statistics and mathematics • Necessary material integrated into the course • Will utilize basic machine learning toolkits such as WEKA and Waffles • Projects may require elementary programming, but each team will include at least one “programmer”

4. WHAT IS DATA MINING? • The process of automatically extracting useful information from large amounts of data. • Uses traditional data analysis techniques (statistics) and sophisticated computer algorithms to discover patterns. • Uses machine learning techniques to find structural patterns within the data.

5. Origins of Data Mining • Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems • Traditional Techniquesmay be unsuitable due to • Enormity of data • High dimensionality of data • Heterogeneous, distributed nature of data Statistics/AI Machine Learning/ Pattern Recognition Data Mining Database systems

6. Cross Industry Standard Process for Data Mining

7. The Process -- Simplified • pre-processing, • data mining • results validation

8. Two Basic Problem Classes • Prediction Methods • Use some variables to predict unknown or future values of other variables. • Description Methods • Find human-interpretable patterns that describe the data.

9. Basic Types of Data Mining Tasks • Classification (predictive) • Clustering (descriptive) • Association rules (descriptive) • Sequential patterns (descriptive or predictive) • Regression (predictive) • Anomaly Detection (predictive)

10. Data Mining Techniques • Statistical techniques • Clustering • Decision trees • Subsampling (bootstrapping) • Nearest-neighborhoods • SOM • Bayesian methods

11. Data Mining Techniques • Artificial Neural Nets • Deep Learning (Google DeepMind) • PCA • Universal Prediction • Reinforcement Learning • “Compression” Sequence Prediction Techniques • Time Series Analysis

12. Data Mining Techniques • Hidden Markov Models • MLN • PLN • EDA (MOSES) • Random Forests • Feature Engineering • Unsupervised and Semi-Supervised Learning

13. DATA MINING TECHNIQUES • Entropy methods • Multifractal methods (time series) • Log-linear power laws (crash prediction) • Wavelet transforms • …. • …. • ….

14. CLASSIFICATION: Definition • Given a collection of records (training set ) • Each record contains a set of attributes • one of the attributes is the class. • Find a model for class attribute as a function of the values of other attributes. • Goal: previously unseen records should be assigned a class as accurately as possible. • A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

15. CLUSTERING: Definition • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that • Data points in one cluster are more similar to one another. • Data points in separate clusters are less similar to one another. • Similarity Measures: • Euclidean Distance if attributes are continuous. • Other Problem-specific Measures.

16. ASSOCIATION RULE: Definition • Given a set of records each of which contain some number of items from a given collection; • Produce dependency rules which will predict occurrence of an item based on occurrences of other items.

17. SEQUENTIAL PATTERN: Definition • Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. • Rules are formed by first discovering patterns. Event occurrences in the patterns are governed by timing constraints.

18. REGRESSION: Definition • Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. • Greatly studied in statistics, neural network fields. • Examples: • Predicting sales amounts of new product based on advetising expenditure. • Predicting wind velocities as a function of temperature, humidity, air pressure, etc. • Time series prediction of stock market indices.

19. ANOMALY DETECTION: Definition • Detect significant deviations from normal behavior • Applications: • Credit Card Fraud Detection • Network Intrusion Detection

20. DATA MINING CHALLENGES • Scalability • Dimensionality • Complex and Heterogeneous Data • Data Quality • Data Ownership and Distribution • Privacy Preservation • Streaming Data