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Intro to Data Mining for Data
Science
Peter Fox, Greg Hughes
Data Science – ITWS/CSCI/ERTH-4350/6350
Module 6, Week 8, October 25, 2016
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Information Technology
and Web Science
Alumni Panel
Alumni will talk about where they are now and how their
degree helped them get there.
Followed by questions and answers.
Date: 10/26/2016
Time: 6:30 pm
Lally 102
Panelists from:
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Light refreshments will be served!
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Contents
• Data Mining what it is, is not, types
• Distributed applications – modern data
mining
• Science example
• A specific toolkit and two examples
– Classifier
– Image analysis – clouds
• Week 9 reading – note is PRE-READING
(only two articles)
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Types of data
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Data Mining – What it is
• Extracting knowledge from large amounts of data
• Motivation
– Our ability to collect data has expanded rapidly
– It is impossible to analyze all of the data manually
– Data contains valuable information that can aid in decision making
• Uses techniques from:
–
–
–
–
–
Pattern Recognition
Machine Learning
Statistics
High Performance Database Systems
OLAP
• Plus techniques unique to data mining (Association rules)
• Data mining methods must be efficient and scalable
Data Mining – What it isn’t
• Small Scale
– Data mining methods are designed for large data sets
– Scale is one of the characteristics that distinguishes data mining
applications from traditional machine learning applications
• Foolproof
–
–
–
–
Data mining techniques will discover patterns in any data
The patterns discovered may be meaningless
It is up to the user to determine how to interpret the results
“Make it foolproof and they’ll just invent a better fool”
• Magic
– Data mining techniques cannot generate information that is not
present in the data
– They can only find the patterns that are already there
Data Mining – Types of Mining
• Classification (Supervised Learning)
– Classifiers are created using labeled training samples
– Training samples created by ground truth / experts
– Classifier later used to classify unknown samples
• Clustering (Unsupervised Learning)
– Grouping objects into classes so that similar objects are in the
same class and dissimilar objects are in different classes
– Discover overall distribution patterns and relationships between
attributes
• Association Rule Mining
– Initially developed for market basket analysis
– Goal is to discover relationships between attributes
– Uses include decision support, classification and clustering
• Other Types of Mining
– Outlier Analysis
– Concept / Class Description
– Time Series Analysis
Data Mining in the ‘new’ Distributed
Data/Services Paradigm
Science Motivation
• Study the impact of natural iron fertilization process
(such as a dust storm) on plankton growth and
subsequent dimethyl sulfide (DMS) production
– Plankton plays an important role in the carbon cycle
– Plankton growth is strongly influenced by nutrient
availability (Fe/Ph)
– Dust deposition is important source of Fe over ocean
– Satellite data is an effective tool for monitoring the effects
of dust fertilization
Hypotheses
• In remote ocean locations there is a positive
correlation between the area averaged
atmospheric aerosol loading and oceanic
chlorophyll concentration
• There is a time lag between oceanic dust
deposition and the photosynthetic activity
Primary source of
ocean nutrients
OCEAN
UPWELLING
WIND
BLOWNDUST
SEDIMENTS
FROM RIVER
SAHARA
CLOUDS
Factors modulating
dust-ocean
photosynthetic effect
SST
CHLOROPHYLL
DUST
NUTRIENTS
SAHARA
Objectives
• Use satellite data to determine, if
atmospheric dust loading and
phytoplankton photosynthetic activity are
correlated.
• Determine physical processes responsible
for observed relationship
Data and Method
• Data sets obtained from two instruments:
SeaWiFS and MODIS during 2000 – 2006
are employed
• MODIS derived AOT (Aerosol Optical
Thickness)
– SeaWIFS - Sea-Viewing Wide Field-of-View
Sensor
– MODIS – Moderate resolution Imaging
Spectrometer
– AOT – Aerosol Optical Thickness
The areas of study
*Figure: annual SeaWiFS chlorophyll image for 2001
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7
6
1
2
3
4
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1-Tropical North Atlantic Ocean 2-West coast of Central Africa 3Patagonia 4-South Atlantic Ocean 5-South Coast of Australia 6-Middle
East 7- Coast of China 8-Arctic Ocean
Tropical North Atlantic Ocean 
dust from Sahara Desert
-0.0902
-0.328
-0.4595
-0.14019
-0.7253
-0.1095
-0.75102
-0.66448
-0.72603
AOT
Chlorophyll
-0.17504
-0.68497
-0.15874
-0.85611
-0.4467
Arabian Sea  Dust from Middle
East
0.66618
0.65211
0.76650
0.37991
0.45171
0.52250
0.36517
0.5618
0.4412
0.75071
0.708625
0.8495
AOT
Chlorophyll
0.59895
0.69797
Summary …
• Dust impacts oceans photosynthetic activity,
positive correlations in some areas NEGATIVE
correlation in other areas, especially in the Saharan
basin
• Hypothesis for explaining observations of negative
correlation: In areas that are not nutrient limited,
dust reduces photosynthetic activity
• But also need to consider the effect of clouds,
ocean currents. Also need to isolate the effects of
dust. MODIS AOT product includes contribution
from dust, DMS, biomass burning etc.
Data Mining – Types of Mining
• Classification (Supervised Learning)
– Classifiers are created using labeled training samples
– Training samples created by ground truth / experts
– Classifier later used to classify unknown samples
• Clustering (Unsupervised Learning)
– Grouping objects into classes so that similar objects are in the
same class and dissimilar objects are in different classes
– Discover overall distribution patterns and relationships between
attributes
• Association Rule Mining
– Initially developed for market basket analysis
– Goal is to discover relationships between attributes
– Uses include decision support, classification and clustering
• Other Types of Mining
– Outlier Analysis
– Concept / Class Description
– Time Series Analysis
Models/ types
• Trade-off between Accuracy and
Understandability
• Models range from “easy to understand” to
incomprehensible
– Decision trees
– Rule induction
– Regression models
– Neural Networks
H
a
r
d
e
r
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Qualitative and Quantitative
• Qualitative
– Provide insight into the data you are working with
• If city = New York and 30 < age < 35 …
• Important age demographic was previously 20 to 25
• Change print campaign from Village Voice to New
Yorker
– Requires interaction capabilities and good
visualization
• Quantitative
• Automated process
• Score new gene chip datasets with error model every
night at midnight
• Bottom-line orientation
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Management
•
•
•
•
•
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Creation of logical collections
Physical data handling
Interoperability support
Security support
Data ownership
Metadata collection, management and
access.
• Persistence
• Knowledge and information discovery
• Data dissemination and publication
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Provenance*
• Origin or source from which something
comes, intention for use, who/what generated
for, manner of manufacture, history of
subsequent owners, sense of place and time
of manufacture, production or discovery,
documented in detail sufficient to allow
reproducibility
ADaM – System Overview
• Developed by the Information Technology and Systems
Center at the University of Alabama in Huntsville
• Consists of over 75 interoperable mining and image
processing components
• Each component is provided with a C++ application
programming interface (API), an executable in support of
scripting tools (e.g. Perl, Python, Tcl, Shell)
• ADaM components are lightweight and autonomous, and have
been used successfully in a grid environment
• ADaM has several translation components that provide data
level interoperability with other mining systems (such as
WEKA and Orange), and point tools (such as libSVM and
svmLight)
• Future versions will include Python wrappers and possible web
service interfaces
ADaM 4.0 Components
ADaM Classification Process
• Identify potential features which may characterize the
phenomenon of interest
• Generate a set of training instances where each instance
consists of a set of feature values and the corresponding class
label
• Describe the instances using ARFF file format
• Preprocess the data as necessary (normalize, sample etc.)
• Split the data into training / test set(s) as appropriate
• Train the classifier using the training set
• Evaluate classifier performance using test set
• K-Fold cross validation, leave one out or other more
sophisticated methods may also be used for evaluating
classifier performance
ADaM Classification Example
• Starting with an ARFF file, the ADaM system will be used to
create a Naïve Bayes classifier and evaluate it
• The source data will be an ARFF version of the Wisconsin
breast cancer data from the University of California Irvine
(UCI) Machine Learning Database:
http://www.ics.uci.edu/~mlearn/MLRepository.html
• The Naïve Bayes classifier will be trained to distinguish
malignant vs. benign tumors based on nine characteristics
Naïve Bayes Classification
• Classification problem with m classes C1, C2, … Cm
• Given an unknown sample X, the goal is to choose a class that
is most likely based on statistics from training data

P(Ci | X) can be computed using Bayes’ Theorem:
[1] Equations from J. Han and M. Kamber, “Data Mining: Concepts and Techniques”,
Morgan Kaufmann, 2001.
Naïve Bayes Classification
• P(X) is constant for all classes, so finding the most likely class
amounts to maximizing P(X | Ci) P(Ci)
• P(Ci ) is the prior probability of class i. If the probabilities are not
known, equal probabilities can be assumed.
• Assuming attributes are conditionally independent:

P(xk | Ci) is the probability density function for attribute k
[1] Equation from J. Han and M. Kamber, “Data Mining: Concepts and Techniques”,
Morgan Kaufmann, 2001.
Naïve Bayes Classification


P(xk | Ci) is estimated from the training samples
Categorical Attributes (non-numeric attributes)
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
Estimate P(xk | Ci) as percentage of samples of class i with value xk
Training involves counting percentage of occurrence of each
possible value for each class
Numeric attributes
–
–
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Also use statistics of the sample data to estimate P(xk | Ci)
Actual form of density function is generally not known, so Gaussian
density is often assumed
Training involves computation of mean and variance for each
attribute for each class
Naïve Bayes Classification

Gaussian distribution for numeric attributes:
–
–
Where
is the mean of attribute k observed in samples of class Ci
And
is the standard deviation of attribute k observed in samples
of class Ci
[1] Equation from J. Han and M. Kamber, “Data Mining: Concepts and Techniques”,
Morgan Kaufmann, 2001.
Sample Data Set – ARFF
Format
Data management
•
•
•
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Metadata?
Data?
File naming?
Documentation?
Splitting the Samples
• ADaM has utilities for splitting data sets into disjoint
groups for training and testing classifiers
• The simplest is ITSC_Sample, which splits the source
data set into two disjoint subsets
Splitting the Samples
• For this demo, we will split the breast cancer data set into two
groups, one with 2/3 of the patterns and another with 1/3 of the
patterns:
ITSC_Sample -c class -i bcw.arff -o trn.arff -t tst.arff –p 0.66
• The –i argument specifies the input file name
• The –o and –t arguments specify the names of the two output
files (-o = output one, -t = output two)
• The –p argument specifies the portion of data that goes into
output one (trn.arff), the remainder goes to output two (tst.arff)
• The –c argument tells the sample program which attribute is the
class attribute
Provenance?
• For this demo, we will split the breast cancer data set into two
groups, one with 2/3 of the patterns and another with 1/3 of the
patterns:
ITSC_Sample -c class -i bcw.arff -o trn.arff -t tst.arff –p 0.66
• What needs to be recorded and why?
• What about intermediate files and why?
• How are they logically organized?
ITSC_Sample -c class -i bcw.arff -o train_p.66.arff –t
bcw_test.33.arff –p 0.66
Training the Classifier
• ADaM has several different types of classifiers
• Each classifier has a training method and an application
method
• ADaM’s Naïve Bayes classifier has the following syntax:
Training the Classifier
• For this demo, we will train a Naïve Bayes classifier:
ITSC_NaiveBayesTrain -c class -i trn.arff –b bayes.txt
• The –i argument specifies the input file name
• The –c argument specifies the name of the class attribute
• The –b argument specifies the name of the classifier file:
Applying the Classifier
• Once trained, the Naïve Bayes classifier can be used to
classify unknown instances
• The syntax for ADaM’s Naïve Bayes classifier is as follows:
Applying the Classifier
• For this demo, the classifier is run as follows:
ITSC_NaiveBayesApply -c class -i tst.arff –b bayes.txt -o res_tst.arff
•
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The –i argument specifies the input file name
The –c argument specifies the name of the class attribute
The –b argument specifies the name of the classifier file
The –o argument specifies the name of the result file:
Evaluating Classifier
Performance
• By applying the classifier to a test set where the correct
class is known in advance, it is possible to compare the
expected output to the actual output.
• The ITSC_Accuracy utility performs this function:
Confusion matrix
Classified\ Actual
0
1
0
TRUE
POSITIVES
FALSE
POSITIVES
1
FALSE
NEGATIVES
TRUE
NEGATIVES
• Gives a guide to accuracy but samples (i.e. bias)
are important to take into account
Evaluating Classifier
Performance
• For this demo, ITSC_Accuracy is run as follows:
ITSC_Accuracy -c class -t res_tst.arff –v tst.arff –o acc_tst.txt
Python Script for
Classification
How would you modify this?
What is the provenance?
ADaM Image Classification
• Classification of image data is a bit more involved, as there is
an additional set of steps that must be performed to extract
useful features from the images before classification can be
performed.
• In addition, it is also useful to transform the data back into
image format for visualization purposes.
• As an example problem, we will consider detection of
cumulus cloud fields in GOES satellite images
– GOES satellites produce a 5 channel image every 15 minutes
– The classifier must label each pixel as either belonging to a
cumulus cloud field or not based on the GOES data
– Algorithms based on spectral properties often miss cumulus
clouds because of the low resolution of the IR channels and the
small size of clouds
– Texture features computed from the GOES visible image provide
a means to detect cumulus cloud fields.
GOES Images Preprocessing
• Segmentation is based only on the high resolution (1km)
visible channel.
• In order to remove the effects of the light reflected from the
Earth’s surface, a visible reference background image is
constructed for each time of the day.
• The reference image is subtracted from the visible image
before it is segmented.
• GOES image patches containing cumulus cloud regions, other
cloud regions, and background were selected
• Independent experts labeled each pixel of the selected image
patches as cumulus cloud or not
• The expert labels were combined to form a single “truth”
image for each of the original image patches. In cases where
the experts disagreed, the truth image was given a “don’t
know” value
GOES Images - Example
GOES Visible Image
Expert Labels
Image Quantization
• Some texture features perform better when the image is
quantized to some small number of levels before the
features are computed.
• ITSC_RelLevel performs local image quantization
Image Quantization
• For this demo, we will reduce the number of levels from 256
to just three using local image statistics:
ITSC_RelLevel –d -s 30 –i src.bin –o q4.bin –k
• The –i argument specifies the input file name
• The –o argument specifies the output file name
• The –d argument tells the program to use standard deviation
to set the cutoffs instead of a fixed value
• The –k option tells the program to keep values in the range
0, 1, 2 rather than normalizing to 0..1.
• The –s argument indicates the size of the local area used to
compute statistics
Computing Texture Features
• ADaM is currently able to compute five different types of
texture features: gray level cooccurrence, gray level run
length, association rules, Gabor filters, and MRF models
• The syntax for gray level run length computation is:
Computing Texture Features
• For this demo, we will compute gray level run length features
using a tile size of 25:
ITSC_Glrl –i q4.bin –o glrl.arff –l 3 –B –t 25
• The –i argument specifies the input file name
• The –o argument specifies the output file name
• The –l argument tells the program the number of levels in
the input image
• The –B option tells the program to write a binary version of
the ARFF file (default is ASCII)
• The –t argument indicates the size of the tiles used to
compute the gray level run length features
Provenance alert!
• For this demo, we will compute gray level run length features
using a tile size of 25:
ITSC_Glrl –i q4.bin –o glrl.arff –l 3 –B –t 25
• What needs to be documented here and why?
– ITSC_Glrl ???? Tell me.
Converting the Label Images
• Since the labels are in the form of images, it is
necessary to convert them to vector form
• ITSC_CvtImageToArff will do this:
Converting ??????
• Since the labels are in the form of images, it is
necessary to convert them to vector form
• Consequences?
• Do you save them?
• Discussion?
Converting the Label Images
• The labels can be converted to vector form using:
ITSC_CvtImageToArff –i lbl.bin –o lbl.arff -B
• The –i argument specifies the input file name
• The –o argument specifies the output file name
• The –B argument tells the program to write the output file in
binary form (default is ASCII)
Labeling the Patterns
• Once the labels are in vector form, they can be
appended to the patterns produced by ITSC_Glrl
• ITSC_LabelPatterns will do this:
Labeling the Patterns
• The labels are assigned to patterns as follows:
ITSC_LabelPatterns –i glrl.arff –c class –l lbl.bin –L lbl.arff –o all.arff –B
• The –i argument specifies the input file name (patterns)
• The –o argument specifies the output file name The –c
argument
• The –c argument specifies the name of the class attribute in
the pattern set
• The –l argument specifies the name of the label attribute in
the label set
• The –L argument specifies the name of the input label file
• The –B argument tells the program to write the output file in
binary form (default is ASCII)
Eliminating “Don’t Know”
Patterns
• Some of the original pixels were classified differently by
different experts and marked as “don’t know”
• The corresponding patterns can be removed from the training
set using ITSC_Subset:
Eliminating “Don’t Know”
Patterns
• ITSC_Subset is used to remove patterns with unclear class
assignment. The subset is generated based on the value of
the class attribute:
ITSC_Subset –i all.arff –o subset.arff –a class –r 0 1 -B
•
•
•
•
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The –i argument specifies the input file name
The –o argument specifies the output file name
The –a argument tells which attribute to test
The –r argument tells the legal range of the attribute
The –B argument tells the program to write the output file in
binary form (default is ASCII)
Selecting Random Samples
• Random samples are selected from the original training data
using the same ITSC_Sample program shown in the
previous demo
• The program is used in a slightly different way:
ITSC_Sample –i subset.arff –c class –o s1.arff –n 2000
•
•
•
•
The –i argument specifies the input file name
The –o argument specifies the output file name
The –c argument specifies the name of the class attribute
The –n option tells the program to select an equal number of
random samples (in this case 2000) from each class.
Python Script for Sample
Creation
What modifications here??
Merging Samples / Multiple
Images
• The procedure up to this point has created a random subset
of points from a particular image. Subsets from multiple
images can be combined using ITSC_MergePatterns:
Merging Samples / Multiple
Images
• Multiple pattern sets are merged using the following
command:
ITSC_MergePatterns –c class –o merged.arff –i s1.arff s2.arff
• The –i argument specifies the input file names
• The –o argument specifies the output file name
• The –c argument specifies the name of the class attribute
Python Script for Training
Results of Classifier
Evaluation
• The results of running this procedure using five sample
images of size 500x500 is as follows:
Applying the Classifier to
Images
• Once the classifier is trained, it can be applied to segment
images. One further program is required on the end to
convert the classified patterns back into an image:
Python Function for
Segmentation
Sample Image Results
Expert Labels
Segmentation Result
Remarks
• The procedure illustrated here is one specific example of
ADaM’s capabilities
• There are many other classifiers, texture features and other
tools that could be used for this problem
• Since all of the algorithms of a particular type work in more or
less the same way, the same general procedure could be
used with other tools
• DOWNLOAD the ADaM Toolkit
– http://datamining.itsc.uah.edu/adam/
Numpy, scipy
• http://scikit-learn.org/stable/
• http://orange.biolab.si
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R
• http://www.rdatamining.com
• R Studio
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Management
• What did you learn?
• Provenance elements?
• How to deal with both?
Reading this week – (week 4)
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Project Teams (A4)
1. Byrne, Corey; Cheng, Jie; Deb, Arijit; De Los Santos, Hannah; Zednik,
Stephan
2. Cattarin, Lee; Del Priore, James; Dwivedi, Aayush; Gadomski, Paula;
Wang, Jixuan; Xu, Zhe
3. Grivas, Genevieve; Gentyala, Vijay; Ho, Chien-Wei; Khachaturyan, David;
Renus, Christopher
4. Falk, Jeremy; Kolankowski, Sophia; Kuzmin, Konstantin; Peshin, Ankur;
Yu, Guo
5. Li, Lilli; Liu, Guohui; Russo, Robert; Sharma, Sidharth; Thorne, Brandon
6. Francis, Kyle; Liu, Yi; Llewellyn, Maxwell; Sedlacek, Aaron; Troughia,
Chandan Singh; You, Charles
7. Gass, Damon; Lu, Yongqian; Machado, Ylonka; Norris, Spencer;
Subhedar Shradda, Arun
8. Maeser, Adrienne; Minster, Zachary; Mukherjee Partha, Sarathi; Pan,
Xiaoman; Tse, Timothy
9. Boulter, James; Nunez, Nicole; Prabhakaran, Sidharth; Qui, Yichen;
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Ward, David
Anyone missing?