1. Course Introduction
CSC 576: Data Mining

2. Today
What is Data Mining?
Syllabus / Course Webpage
Types of Data

3. What is Data Mining?
How would you define data mining?
Data Mining and Business Analytics deal with collecting and analyzing data for better decision making.
Goal: solving business problems
Data collection (more and more data is being collected)
Warehousing of data (readily available for analysis; data from numerous sources already integrated)
Computer storage and computer power cheaper every day
Good software for performing analysis
(prompt)

4. Data Mining
… blends traditional data analysis (mathematical + statistical) with sophisticated machine learning algorithms
Programming ability to process big data
Businesses interested in decision making
“Art” of data mining
Math
Business CS

5. Predictive Data Mining
Moving from data to insights to decisions.

6. Data Mining Applications
Businesses collect lots of data:
Purchase information
Web site browsing habits
Social network data
Business Goals:
customer profiling, targeted marketing, fraud detection
Questions that analyst will try to answer by data mining:
“Who are the most profitable customers?”
“What products can be cross-sold?”
“What is the revenue outlook for the company next year?”
Many variables are collected; few turn out to be useful.

7. More Applications Price Prediction Fraud Detection Risk Assessment
Diagnosis

8. What we will do in this Course
Learn Basic-to-Intermediate Data Mining Techniques
Apply them on Datasets
Program using Python
Read, Understand, Discuss, Critique Scientific Papers
Perform Significant Individual Data Mining Project

9. Syllabus / Course Webpage

10. “looking up records in a MySQL database” (database)
What is Data Mining?
What is NOT data Mining?
“the process of automatically discovering useful information in large data repositories”
“to find novel and useful patterns that might otherwise remain unknown”
“looking up records in a MySQL database” (database)
“finding relevant web pages based on a Google search query” (information retrieval)

11. Data Mining and Knowledge Discovery
Process of converting raw data into useful information
Input Data
MySQL
.csv
JSON
Twitter API
Data Preprocessing
Feature Selection
Dimensionality Reduction
Normalization
Data Mining
Decision Trees
Support Vector Machines
Linear Regression
Neural Networks
Postprocessing
Visualization
Pattern Interpretation
Reporting to Boss
“closing the loop”

12. Input Data Available in data in variety of formats:
Flat files (.csv or .txt)
Spreadsheets (Excel .xls tougher to deal with)
Relational tables (MySQL)
Text, data on web page (scraping necessary)
Big Data / Data Warehouse
Data spread out over multiple locations
CS programming ability often necessary
Sometimes enormous amount of effort
Digitizing hand-written notes

13. Preprocessing
To transform raw input data into an appropriate format for subsequent analysis
Fusing data from multiple sources
Cleaning data to remove noise
Duplicate observations
“garbage in – garbage out” also applies to data mining
Selecting records and features that are relevant to the data mining task at hand

14. Data Mining Applying Appropriate Data Mining Task Linear Regression
Support Vector Machines
Decision Trees
Clustering …

15. Postprocessing Performing: Visualization
Statistical significant tests, confidence intervals, hypothesis testing to eliminate spurious data mining results
(yikes, math!)

16. Challenges of Data Mining
Scalability
Gigabytes, terabytes, petabytes, exabytes of data
Storage, processing
“are data mining algorithms scalable?”
Limits of python statistical framework libraries

17. Challenges of Data Mining
High Dimensionality
Datasets with hundreds or thousands of attributes
Some traditional data analysis techniques were developed for low-dimensional data, and many not work well with high-dimensional data
Many variables are collected; few turn out to be useful.

18. Challenges of Data Mining
Heterogeneous and Complex Data
Traditional data analysis often deals with data sets containing attributes of the same type (e.g. all continuous, all categorical)
Non-traditional data: collection of web pages (w/ semi-structured text and hyperlinks)

19. Challenges of Data Mining
Data Ownership
“Good data” being geographically distributed owned by more than one organization (e.g. medical records)
Access to “good data”
Facebook and google keep their collected data private

20. Sample Data Vocabulary:
What is interesting in this data?
Vocabulary:
Column: “attribute”, “feature”, “field”, “dimension”, “variable”
Row: “instance”, “record”, “observation”

21. Data Mining Tasks Predictive Tasks
Objective: predict value of a particular attribute, based on the values of other attributes
“Defaulted Barrower?” is the target (or dependent variable)
Attributes/features used for making the prediction are known as explanatory (or independent variables)

22. Supervised Machine Learning
Machine Learning techniques automatically learn a model of the relationship between a set of descriptive features and a target feature from a set of historical examples.

23. Data Mining Tasks Descriptive Tasks
Objective: derive patterns (correlations, clusters) that summarize underlying relationships in data
Often more exploratory and requires an explanation of found results

24. “Free Public Datasets”
datasets
26/big-data-and-ai-30-amazing-and-free-public- data-sources-for-2018/

25. References
Fundamentals of Machine Learning for Predictive Data Analytics, 1st Edition, Kelleher et al.
Data Science from Scratch, 1st Edition, Grus
Introduction to Data Mining, 1st edition, Tan et al.
Data Mining and Business Analytics in R, 1st edition, Ledolter