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ADVENTURES IN DATA MINING
Margaret H. DunhamSouthern Methodist University
Dallas, Texas [email protected]
This material is based in part upon work supported by the National Science Foundation under Grant No. 9820841
Some slides used by permission from Dr Eamonn Keogh; University of California Riverside;[email protected]
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The 2000 ozone hole over the antarctic seen by EPTOMS
http://jwocky.gsfc.nasa.gov/multi/multi.html#hole
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Data Mining Outline
Introduction Techniques
Classification Clustering Association Rules
Examples
Explore some interesting data mining applications
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Introduction
Data is growing at a phenomenal rate Users expect more sophisticated information How?
UNCOVER HIDDEN INFORMATIONDATA MINING
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But it isn’t Magic
You must know what you are looking for You must know how to look for you
Suppose you knew that a specific cave had gold:• What would you look for?• How would you look for it?• Might need an expert miner
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“If it looks like a duck, walks like a duck, and quacks like a duck, then
it’s a duck.”
Description Behavior AssociationsClassification Clustering Link Analysis (Profiling) (Similarity)
“If it looks like a terrorist, walks like a terrorist, and quacks like a terrorist, then
it’s a terrorist.”
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CLASSIFICATION
Assign data into predefined groups or classes.
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Classification Ex: Grading
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>=90<90x
>=80<80
x
>=70<70
x
F
B
A
>=60<50
x C
D
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Grasshoppers
Katydids
Given a collection of annotated data. (in this case 5 instances of Katydids and five of Grasshoppers), decide what type of insect the unlabeled example is.
(c) Eamonn Keogh, [email protected]
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Insect ID Abdomen Length
Antennae Length
Insect Class
1 2.7 5.5 Grasshopper
2 8.0 9.1 Katydid
3 0.9 4.7 Grasshopper
4 1.1 3.1 Grasshopper
5 5.4 8.5 Katydid
6 2.9 1.9 Grasshopper
7 6.1 6.6 Katydid
8 0.5 1.0 Grasshopper
9 8.3 6.6 Katydid
10 8.1 4.7 Katydid
11 5.1 7.0 ???????
The classification problem can now be expressed as:
• Given a training database predict the class label of a previously unseen instance
previously unseen instance = (c) Eamonn Keogh, [email protected]
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Ant
enna
Len
gth
10
1 2 3 4 5 6 7 8 9 10
123456789
Grasshoppers Katydids
Abdomen Length
(c) Eamonn Keogh, [email protected]
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Handwriting Recognition
George Washington Manuscript
0 50 100 150 200 250 300 350 400 4500
0.5
1
(c) Eamonn Keogh, [email protected]
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Rare Event Detection
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Dallas Morning News
October 7, 2005
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CLUSTERING
Partition data into previously undefined groups.
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192/25/09 - GCSUhttp://149.170.199.144/multivar/ca.htm
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Two Types of Clustering
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Hierarchical Partitional
(c) Eamonn Keogh, [email protected]
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Hierarchical Clustering ExampleIris Data Set
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Setosa
Versicolor
Virginica• The data originally appeared in Fisher, R. A. (1936). "The Use of
Multiple Measurements in Axonomic Problems," Annals of Eugenics 7, 179-188.
• Hierarchical Clustering Explorer Version 3.0, Human-Computer Interaction Lab, University of Maryland, http://www.cs.umd.edu/hcil/multi-cluster .
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ASSOCIATION RULES/ LINK ANALYSIS
Find relationships between data
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ASSOCIATION RULES EXAMPLES
People who buy diapers also buy beer If gene A is highly expressed in this disease then
gene A is also expressed Relationships between people Book Stores Department Stores Advertising Product Placement http://
www.amazon.com/Data-Mining-Introductory-Advanced-Topics/dp/0130888923/ref=sr_1_1?ie=UTF8&s=books&qid=1235564485&sr=1-1
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Data Mining Introductory and Advanced Topics, by Margaret H. Dunham, Prentice Hall, 2003.
DILBERT reprinted by permission of United Feature Syndicate, Inc.
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Data Mining Outline
Introduction TechniquesExamples
Vision Mining Law Enforcement (Cheating,
Plagiarism, Fraud, Criminal Behavior,…)
Bioinformatics2/25/09 - GCSU
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Vision Mining
License Plate Recognition Red Light Cameras Toll Booths http://www.licenseplaterecognition.com/
Computer Vision http://www.eecs.berkeley.edu/Research
/Projects/CS/vision/shape/vid/
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How Stuff Works, “Facial Recognition,” http://computer.howstuffworks.com/facial-recognition1.htm
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Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.
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No/Little Cheating
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Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.
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Rampant Cheating
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Joshua Benton and Holly K. Hacker, “At Charters, Cheating’s off the Charts:, Dallas Morning News, June 4, 2007.
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Jialun Qin, Jennifer J. Xu, Daning Hu, Marc Sageman and Hsinchun Chen, “Analyzing Terrorist Networks: A Case Study of the Global Salafi Jihad Network” Lecture Notes in Computer Science, Publisher: Springer-Verlag GmbH, Volume 3495 / 2005 , p. 287.
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http://www.time.com/time/magazine/article/0,9171,1541283,00.html
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DNA
Basic building blocks of organisms Located in nucleus of cells Composed of 4 nucleotides Two strands bound together
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http://www.visionlearning.com/library/module_viewer.php?mid=63
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Central Dogma: DNA -> RNA -> Protein
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Protein
RNA
DNA
transcription
translation
CCTGAGCCAACTATTGATGAA
Amino Acid
CCUGAGCCAACUAUUGAUGAA
www.bioalgorithms.info; chapter 6; Gene Prediction
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Human Genome
Scientists originally thought there would be about 100,000 genes
Appear to be about 20,000 WHY?
Almost identical to that of Chimps. What makes the difference?
Answers appear to lie in the noncoding regions of the DNA (formerly thought to be junk)
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RNAi – Nobel Prize in Medicine 2006
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Double stranded RNA
Short Interfering RNA (~20-25 nt)
RNA-Induced Silencing Complex
Binds to mRNA
Cuts RNA
siRNA may be artificially added to cell!
Image source: http://nobelprize.org/nobel_prizes/medicine/laureates/2006/adv.html, Advanced Information, Image 3
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miRNA
Short (20-25nt) sequence of noncoding RNA Known since 1993 but significance not widely
appreciated until 2001 Impact / Prevent translation of mRNA Generally reduce protein levels without
impacting mRNA levels (animal cells) Functions
Causes some cancers Guide embryo development Regulate cell Differentiation Associated with HIV …
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TCGR – Mature miRNA(Window=5; Pattern=3)
All Mature
Mus Musculus
Homo Sapiens
C Elegans
ACG CGC GCG UCG2/25/09 - GCSU
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TCGRs for Xue Training Data
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POSITIVE
NEGATIVE
C. Xue, F. Li, T. He, G. Liu, Y. Li, nad X. Zhang, “Classification of Real and Pseudo MicroRNA Precursors using Local Structure-Sequence Features and Support Vector Machine,” BMC Bioinformatics, vol 6, no 310.
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Affymetrix GeneChip® Array
http://www.affymetrix.com/corporate/outreach/lesson_plan/educator_resources.affx
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Microarray Data Analysis
Each probe location associated with gene Measure the amount of mRNA Color indicates degree of gene expression Compare different samples (normal/disease) Track same sample over time Questions
Which genes are related to this disease? Which genes behave in a similar manner? What is the function of a gene?
Clustering Hierarchical K-means
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Microarray Data - Clustering
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"Gene expression profiling identifies clinically relevant subtypes of prostate cancer"Proc. Natl. Acad. Sci. USA, Vol. 101, Issue 3, 811-816, January 20, 2004
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BIG BROTHER ? Total Information Awareness
http://infowar.net/tia/www.darpa.mil/iao/index.htm http://www.govtech.net/magazine/story.php?id=45918 http://en.wikipedia.org/wiki/Information_Awareness_Office
Terror Watch List http://www.businessweek.com/technology/content/may2005/tc20050511_
8047_tc_210.htm http://www.theregister.co.uk/2004/08/19/senator_on_terror_watch/ http://blog.wired.com/27bstroke6/2008/02/us-terror-watch.html
CAPPS http://www.theregister.co.uk/2004/04/26/airport_security_failures/ http://www.heritage.org/Research/HomelandDefense/BG1683.cfm http://www.theregister.co.uk/2004/07/16/homeland_capps_scrapped/ http://en.wikipedia.org/wiki/CAPPS
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http://ieeexplore.ieee.org/iel5/6/32236/01502526.pdf?tp=&arnumber=1502526&isnumber=32236
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