This document outlines a course on data mining and creative research practices. It includes the course outline which covers foundations of data mining like introduction, survey methods, visualization and analysis. It also covers methods like creativity and brainstorming, prototyping, project management. Later sections will cover refining prototypes and project presentations. It provides an overview of common data mining techniques like clustering, classification, regression, association rules and anomaly detection. It discusses how to apply these techniques and what questions they aim to answer from the data. The document emphasizes exploring data to find interesting patterns and questions to ask. It also discusses cleaning and processing data as part of the data mining process.

1. Data Mining
2114.409: Creative Research Practice
HTTP://WWW.FLICKR.COM/PHOTOS/CPBILLS/2888144434/

2. Reflection
Homework 2
Status?
Auditors
Concerns
Programming
What can we build
HTTP://WWW.FLICKR.COM/PHOTOS/FLOWER87/76719859/

3. Course Outline
1. Foundations 3. Prototyping
Introduction Crawling
Survey Methods / Data Mining Text Mining
Visualization and Analysis To be determined (TBD)
Social Mechanics Project Update
2. Methods 4. Refinement
Creativity and Brainstorming TBD x3
Prototyping Project Presentations
Project Management Reflection

4. Data Mining Overview
How do I see and
communicate answers?
Lecture 2, HW2
What questions should
I ask of the data?
Today, HW3
on-demand
How do I clean and
process the data?
How do I gather
Later?
meaningful data?

5. THIS LECTURE BARELY SCRATCHES THE
SURFACE OF INFORMATION VISUALIZATION.
IT IS A JUMPING OFF POINT.

6. Data Mining Overview
How do I see and
communicate answers?
Lecture 2, HW2
What questions should
I ask of the data?
Today, HW3
on-demand
How do I clean and
process the data?
How do I gather
Later?
meaningful data?

7. Data Exploration
Often the questions are not obvious and it’s
useful to look at the data for inspiration.

8. Exploration: Data Cubes
Basic operations:
‣ Group
(how to chunk data)
‣ Summarize
(sum, mean, etc.)
‣ Filter
(which rows to include)

9. Pivot Table Tutorial

10. Data Mining Overview
How do I see and
communicate answers?
Lecture 2, HW2
What questions should
I ask of the data?
Today, HW3
on-demand
How do I clean and
process the data?
How do I gather
Later?
meaningful data?

11. Objectives
DATA MINING EACH TECHNIQUE
‣ What is it? ‣ What is it doing?
‣ How does it relate to ‣ Why is it useful?
collective intelligence?
‣ How might you apply it?

12. Are there patterns in the data?
HUMAN VISUAL vs.
COMPUTER
SYSTEM ANALYSIS

13. Why might we prefer analysis?
LABOR ACCURACY
Too many pictures to look at. Can test for statistical
significance, etc.
Don’t know which are
interesting. Some patterns don’t
visualize easily.
HTTP://WWW.FLICKR.COM/PHOTOS/STRIATIC/2144933705/

14. Common Techniques
Clustering Classification & Regression
Association Rules Anomaly Detection
HTTP://WWW.FLICKR.COM/PHOTOS/EXPLORATIVEAPPROACH/3866580875/

15. Clustering
Find natural
groupings in
the data
Organize data into classes:
‣ high intra-class similarity
‣ low inter-class similarity

16. Clustering
Input Data Output Clusters
Points Hard
OR
OR
Soft
Similarities OR
[ # of clusters ] Hierarchical

17. K-Means
5
4
k1
3
2
k2
1
k3
0
0 1 2 3 4 5

18. K-Means
5
4
k1
3
2
k2
1
k3
0
0 1 2 3 4 5

19. K-Means
5
4
k1
3
2
k3
1 k2
0
0 1 2 3 4 5

20. K-Means
5
4
k1
3
2
k3
1 k2
0
0 1 2 3 4 5

21. K-Means
5
expression in condition 2
4
k1
3
2
k2
1 k3
0
0 1 2 3 4 5
expression in condition 1

22. Classification Regression
Learn to map objects to Learn map objects to
categories continuous variables

23. Typical Applications
Speech Handwriting OCR

24. Classification
Observations X Learn f(x) = y
Labels Y
Y = gender
Male
Female
X = height

25. The Whole Process
Data Set
Featurization
Featurized
Random Split (e.g. 90/10)
Training Data Test Data
Training
Model
Evaluation
Results

26. Real-World Classification
Observations X Y - 100’s of labels
X - 1000’s of features
Labels Y N - Millions of examples
? - Not all data is labeled
? - Some data is mis-labeled
f(x) = y Model spatial context
Model temporal context

27. Association Rules
Learn interesting
relations in the data
= proportion of events in which X occurs

28. Anomaly Detection
Detect strange
events in the data

29. Homework: Data Mining
1. Form groups!
2. Choose a Collective Intelligence topic from
Lecture 1, or propose similar.
3. Make a list of data sources that might
provide insights to that topic.
4. Propose a set of meaningful questions about
the data based on your intuition.
5. How would you have to clean/process your
data to start answering those questions?
6. Consider clustering, association rules,
anomaly detection, classification. For each
technique, how might you apply it to the
data and what would it show?
7. Document your work and be prepared to
present.
HTTP://WWW.FLICKR.COM/PHOTOS/31907740@N00/4860840019/

30. Data Mining Overview
How do I see and
communicate answers?
Lecture 2, HW2
What questions should
I ask of the data?
Today, HW3
on-demand
How do I clean and
process the data?
How do I gather
Later?
meaningful data?

31. Guest Lecture

32. Feedback