1. Data Mining and Information Visualization Yan Liu, PhD Assistant Professor Department of Biomedical, Industrial and Human Factors Engineering Wright State University

2. 2 Outline Data Mining (DM)  Definition and Usefulness  DM Process  DM Modeling Techniques Information Visualization  Definition and Usefulness  Multivariate Data Visualization Techniques

3. 3 Data Mining (DM): What and Why What Is DM  A synonym for knowledge discovery in databases (KDD)  Nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data (Fayyard et al., 1996)  Lying at the interface of database management, machine learning, pattern recognition, statistics and visualization Why Is DM Useful  Rapid development in information techniques produces vast amounts of data  Knowledge discovered from data can be use for competitive advantage Classification, prediction, association, clustering, etc.

4. 4 Data Mining Process Problem Understanding Data Understanding ModelingEvaluation Deployment Data Preparation Data CRISP-DM(CRoss Industry Standard Process for DM) (Holsheimer,1999)

5. 5 Problem Understanding  Understand the objectives  Define performance criteria Objective or subjective  Assess current situations of the organization Background knowledge, data sources, resources, etc. Data Understanding  Collect data From scratch or existing databases  Describe data Volume, identities of attributes, format, etc.  Explore/survey data Distributions of attributes, relations among a small number of attributes, results of simple aggregations, etc. Statistical analyses, data visualization, database queries can be useful tools  Verify data quality Incomplete data, missing values, errors, etc. Data Mining Process (Cont’d)

6. 6 Data Preparation  “Garbage in, garbage out”  Select data Based on relevance, technical constraints  Clean data remove errors, fill in missing data with default values or estimates by modeling  Construct data Generate new attributes (records), merge tables, transform data, etc.  Reduce data Obtain a dataset much smaller yet retaining enough important information Data Mining Process (Cont’d)

7. 7 Modeling  Select appropriate modeling techniques  Generate test design Test models’ quality and validity  Build models  Assess models According to domain knowledge, success criteria and test design Evaluation  Evaluate results With respect to the project objectives  Review process Overlooked important factors or tasks Deployment  Plan deployment  Plan monitoring and maintenance  Produce final result Data Mining Process (Cont’d)

8. Class Description Classes  e.g. Customers of a bank can be classified into those with “good Credit” and “bad credit”; Grades of students in a class include “A”, “B”, “C”, and “D” Data Characterization  Summarize the data in each class  e.g. summarize the distributions of age, educational level, and household income of customers that have “good credit” or “bad credit” Data Discrimination  Compare data in different classes  e.g. compare customers with “good credit” and those with “bad credit” in their distributions of o age, educational level, and household income 8

9. Mining Frequent Pattern, Associations, and Correlations Frequent Patterns  Patterns that occur frequently in data Itemsets: a set of items that frequently appear together in a transactional dataset Subsequences: a set of events that frequently occur in a particular sequence Substructures: a set of structures (such as graphs, trees, lattices) that appear frequently Association Mining  Discovery of frequent patterns, associations and correlations 9 Computer => Software (support=1%, confidence=50%) Age(20,29] and Income(20K, 29K] => CD Player (support=2%, confidence=60%) Association Rules

10. Classification and Prediction Classification  Process of finding a model that describes and distinguishes data classes, for the purpose of being able to use the model to predict the class of objects whose class label (categorical, unordered) is unknown Numeric Prediction  Models continuous-valued functions to predict the missing or unavailable numerical data values 10

11. Cluster Analysis Functions  Analyze data without consulting a known class label  Divide data into groups(clusters) so that objects within the same cluster are similar while those belonging to different clusters differ much 11

12. Outlier Analysis Function  Identify objects that do not comply with the general pattern of the data 12 Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred by the same account

13. Evolution Analysis Function  Describes and models regularities or trends for objects whose behavior changes over time 13 Suppose you have the major stock market (time-series) data of the last several years available from the New York Stock Exchange and you would like to invest in shares of high-tech industrial companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing to your decision making regarding stock investments

14. 14 Decision Tree Predictive model in a Tree Structure  Decision nodes (splitting attributes) and leaf nodes Leaf Nodes Decision Nodes

15. 15 Association Rules Association Rules Modeling  Finds interesting associations or correlation relationships among items (binary attributes)  In the form of “ if-then ” statements  Measures Support (A=>B) = Pr (A and B) Confidence (A=>B) = Pr (B|A) => Thursdays Antecedent => Consequent => +

16. 16 Information Visualization: What and Why What Is Information Visualization  Use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card,1999) Why Is Information Visualization Useful  Take advantage of the powerful processing capacities of human visual perception system  Three Types of Usages Exploratory analysis: searching for interesting phenomena in data Confirmatory analysis: validating some hypothetical features in data Presentation: demonstrating known information

17. 17 Multivariate Data Visualization Multivariate Data Visualization Methods  Scatterplot matrix  Trellis display  Parallel coordinates  Mosaic display  …

18. 18 Datasets Auto-Mpg Dataset  Retrieved from the UCI machine learning repository  Attributes: “mpg(continuous)”, “cylinders(3/4/5/6/8)”, “horsepower(continuous)”, “weight(continuous)”, “origin(American/European/Japanese)”  392 records Titanic Survival Dataset  Retrieved from Friendly (1994)  Attributes: “booking class (first/second/third/crew)”, “gender (male/female)”, “age (adult/child)”, “survival (yes/no)” Mosaic

19. Scatterplot Matrix Organizes all the pairwise scatterplots in a matrix format Each display panel in the matrix is identified by its row and column coordinates  The panel at the ith row and jth column is a scatterplot of X j versus X i Scatterplot matrix with three variables X, Y, and Z The panel at the 3 rd row (the top row) and 1 st column is a scatterplot of Z versus X Panels that are symmetric with respect to the XYZ diagonal have the same variables as their coordinates, rotated 90° The redundancy is designed to improve visual linking Patterns can be detected in both horizontal and vertical directions Can only visualize the correlation between two variables, without using retinal visual elements 19

20. 20 Scatterplot Matrix of the Auto-Mpg Dataset AmericanEuropean Japanese

21. 21 Trellis Display Overview (Becker and Cleveland, 1996)  Display any one of a large variety of 1-D, 2-D and 3-D plot types in an trellis layout of panels, where each panel displays the select plot type for a level or interval on additional discrete or continuous conditioning variables  Panels are laid out into columns, rows and pages Mapping of Variables and Data Records  Axis variable Mapped to one of the coordinates in the panels  Conditioning variable Mapped to a horizontal bar at the top of each panel, representing on of its levels (discrete variable) or interval (continuous variable)  Superpose variable Mapped to colors or symbols of points in the panels

22. 22 Trellis Display of the Auto-Mpg Dataset AmericanEuropean Japanese

23. 23 Parallel Coordinates Overview (Inselberg, 1985)  Each variable is represented by a vertical axis and m variables are organized as uniformly spaced vertical lines  A data record in a m-D space is manifested as a connected set of points, one on each axis Mapping of Variables and Data Records  Variable X i is represented as ith vertical axis in a 2-D space  Values of X i are scaled so that its maximum and minimum values correspond to the top and bottom points on its axis, respectively  A data record with m variables is represented as a set of m-1 connected line segments which connect to vertical lines at the corresponding variables’ values

24. 24 Parallel Coordinates of the Auto-Mpg Dataset AmericanEuropean Japanese Cylinders mpg HorsepowerWeight Origin

25. 25 Mosaic Display Overview  Well recognized visualization method for categorical variables (Friendly, 1994)  Shows the frequencies in an m-way contingency table by nested rectangles whose areas are proportional to the frequency in cells or marginal subtables  For two or more variables, the levels of sub-division are spaced with larger gaps at the earlier levels to allow easier perception of the groupings at various levels Mosaic Display of the Titanic Survival Dataset survived people not survived people Dataset