This document provides an introduction to data mining. It defines data mining as the process of exploring and analyzing large amounts of data to discover meaningful patterns. It discusses some common data mining techniques such as classification, regression, clustering, and association rule mining. It also introduces some popular data mining tools like R, SAS Enterprise Miner, and XLMiner. Finally, it mentions some notable researchers in the field of data mining.

1. Introduction to Data Mining
for Newbies
Nov. 2th, 2012
@echojuliett

2. Google Datacenter
@Douglas County, Georgia
“These colorful pipes send and receive water for cooling our facility.
Also pictured is a G-Bike, the vehicle of choice for team members to get
around outside our data centers.”
Source: http://www.google.com/about/datacenters/gallery/#/tech/10

3. Eunjeong Lucy Park
PhDs, Data scientist @SNU DMLab
A person who live on lattes.
Find me at:
http://dmlab.snu.ac.kr, http://lucypark.kr
3

4. “All scientists are data scientists.”
- Monica Rogati, Senior Research Scientist @LinkedIn
Source: http://xkcd.com/242/ 4

5. “Data is everywhere.”
Tweets
Cell phone logs
Social networking data
Politician data
Web documents
Manufacturing fault data Credit card transactions
5

6. “Data mining is…”
• “…the process of exploration an analysis, by automatic or semi-automatic means,
of large quantities of data in order to discover meaningful patterns and rules.”
- Berry and Linoff, 1997
Source: Berry and Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, New York: Wiley, 1997.
6

7. “Data mining is…”
• “…the belief in data.”
- @echojuliett, 2012
• Inductive reasoning
 Mathematical induction: prove for k=1, assume for k, then prove for k+1
 Induction vs. prejudice: # of cases
 Ex: What is your hobby?
7

8. “Data mining is…”
8

9. 1. Basic Concepts of Data Mining
2. Origins of Data Mining
3. Data Mining Tools
4. Masters of Data Mining
9

10. Data types
Source: http://www.tipforest.com/t/83
Structured data Unstructured data

11. (the general) Data mining process
Interpretation
Data mining
Preprocessing KNOWLEDGE
Selection
Target data
Patterns
Preprocessed
DATA data
warehouse
of somewhat domain (Marketing, Finance, Manufacturing, etc.)

12. Selection
• Data exploration
– How many variables?
• Independent variables, dependent variables, …
• Continuous variables, categorical variables, …
– How many records?
– What distribution?
– …
• Variable selection & dimensionality reduction
– Ex: Step-wise selection, PCA (Principal Component Analysis)

13. Preprocessing
• “Partitioning” the data
– training data & validation data (& test data …)
Data set
Training data Validation data

14. Preprocessing
• Beware of “overfitting”
Source: Bishop, PRML, p.7

15. Data mining methods
Predictive methods Descriptive methods
Classification Clustering
Learns a method for predicting the instance Finds “natural” grouping of instances given
class from pre-labeled (classified) instances un-labeled data
Regression Association Rules
Method for discovering interesting
An attempt to predict a continuous attribute relations between variables in large DBs

16. Regression
• Linear regression, k-nearest neighbors(k-NN), artificial neural networks (ANN),
…
• Polynomial curve fitting
• The basic form
min
• The advanced form
min
• Example:
• Tomorrow’s stock price = f (recent prices, economic indicators, …)

17. Classification
• Regression with a categorical dependent variable
• Naïve Bayes classification, decision trees, ANNs, SVMs,…
• Ex: E-mail spam detection
inbox
?
spam

18. Clustering
• Grouping of similar objects
• Unsupervised, Exploratory Knowledge Discovery
• k-means, hierarchical clustering, SOM, …
• Ex: Politician segmentation
J ac c ard Sim ilarit y bas ed H ierarc hic al C lus t ering D endrogram (D 9)
0. 8
0. 7
0. 6
0. 5
0. 4
0. 3
0. 2
0. 1
0
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326312297 7720619 268277195262 75 10198 9978 20713096 253318 136255194243 250143179188 20 177154285266 213122 51 1724 30 1510 271291 59
321315299 128237183234204 86 1271002387 28 90 23540307 126 2 13 225231259120 67 71 156202 261198209150 10338 52 286 11 155 7 36 148292309
320295301 31326482 281263 264 89 169 170240 233146159 4 313 16 44 208161163 4816726929 25863252 56 47 175 42 68 107 118221 5 14714 134305 88
325296319 84 265260192 256 244 178 276 273279 257 55 308 91 9 6137 270 232220280272106 50 242 49 4154 249149 12 26
317304324129
316303288168 22 28327893 211 197 152 92 97 34 214 31 145
311302289 13116422419379 199 181 85
160200 171189217 18781 18433 300 95 314 70 196153 65 62 58 245 246 215108112287 166 157 222 135227 43 8 66 124 123
282 210 290218 14020115825114283 236241 162 239 25 113274 228 21 109 102 39
116254104 60 223 144180 110139115 105190 219119 284111
73 247151121293
138114328
275327306
Democratic United Party Grand National Party Others
(liberal) (conservative)

19. Association Rules
Source: http://lucypark.tistory.com/48

20. Data mining methods
Predictive methods Descriptive methods
Classification Clustering
Learns a method for predicting the instance Finds “natural” grouping of instances given
class from pre-labeled (classified) instances un-labeled data
Regression Association Rules
Method for discovering interesting
An attempt to predict a continuous attribute relations between variables in large DBs

21. Pop quiz!
21

22. Pop quiz!
22

23. Pop quiz!
23

24. Pop quiz!
24

25. Pop quiz!
Source: http://www.cis.hut.fi/research/som-research/worldmap.html
25

26. Pop quiz!
Source: http://popupcity.net/2009/04/why-are-that-many-logos-blue/
26

27. Pop quiz!
27

28. 1. Basic Concepts of Data Mining
2. Origins of Data Mining
3. Data Mining Tools
4. Masters of Data Mining
28

29. Historical Note
Data Fishing, Data Dredging: 1960-
• used by statisticians (as a bad name)
Knowledge Discovery in Databases (KDD): 1989-
• used by Artificial Intelligence (AI), Machine Learning (ML) communities
Data Mining, Data Analytics: 1990-
• used in DB communities, business
Big data: 2000-

30. Comparisons
• Data mining
• Statistics
• Machine learning
• Pattern recognition
• …

31. 1. Basic Concepts of Data Mining
2. Origins of Data Mining
3. Data Mining Tools
4. Masters of Data Mining
31

32. R
Source: http://www.kdnuggets.com/2012/05/top-analytics-data-mining-big-data-software.html

33. SAS Enterprise Miner (“E-miner”)

34. XLMiner
• 15-day trial version available at http://www.solver.com/xlminer-data-mining
• Useful for prototyping
• Supports:
• Preprocessing
• Data partitioning
• Missing data imputation
• Categorical data transformation
• PCA (Principal Component Analysis)
• Algorithms
• Multiple linear regression
• k-NN (k nearest neighbors)
• CART (classification and regression trees)
• ANN (artificial neural networks)
• Discriminant analysis
• logistic regression
• Naïve Bayes classification
• Association rules
• k-means clustering
• Hierarchical clustering

35. More…
• Mathworks MATLAB / GNU Octave
 Most DM algorithms are preinstalled
 Relatively easy to learn
• General purpose programming languages
 For example, C, Java, Python, etc.
 Packages such as Orange(http://orange.biolab.si/) for Python are available
 May be more fit for tasks like natural language processing
• Even more…
 Try visiting http://www.kdnuggets.com/software/suites.html

36. 1. Basic Concepts of Data Mining
2. Origins of Data Mining
3. Data Mining Tools
4. Masters of Data Mining
36

37. Foreign warriors
• Mitchell (Carnegie Mellon University)
• Vapnik (NEC Labs)
• Bishop (Microsoft Cambridge)
• Smola (Yahoo, Australian National University)
• Ng (Stanford University)

38. Foreign warriors
• 조성준 (서울대)
• 조재희 (광운대)
• 조성배 (연세대)
• 이성임 (단국대)
• 김성범 (고려대)

39. References
• [1] Duda, Hart, Stork, Pattern Classification 2nd ed., Wiley, 2001.
• [2] Bishop, Pattern Recognition and Machine Learning (PRML), Springer, 2006.
• [3] Shmueli, Patel, Bruce, Data Mining for Business Intelligence, 2nd ed., Wiley, 2010

40. Any Questions?
?