Olfa Nasraoui
Dept. of Computer Engineering & Computer Science
Speed School of Engineering, University of Louisville
Contact e-mail: olfa.nasraoui@louisville.edu
This work is supported by NSF CAREER Award IIS-0431128,
NASA Grant No. AISR-03-0077-0139 issued through the Office of
Space Sciences, NSF Grant IIS-0431128, Kentucky Science & Engr.
Foundation, and a grant from NSTC via US Navy.
Outline of talk
 Data Mining Background
 Mining Footprints Left Behind by Surfers on the Web
 Web Usage Mining: WebKDD process, Profiling &
Personalization
 Mining Illegal Contraband Exchanges on Peer to Peer
Networks
 Mining Coronal Loops Created from Hot Plasma
Eruptions on the Surface of the Sun
Too Much Data!
 There is often information “hidden” in the data that is
not always evident
 Human analysts may take weeks to discover useful
information
 Much of the data is never analyzed at all
4,000,000
3,500,000
Total new disk (TB) since 1995
3,000,000
The Data Gap
2,500,000
2,000,000
1,500,000
1,000,000
Number of
analysts
500,000
0
1995
1996
1997
1998
1999
From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”
Data Mining
 Many Definitions
 Non-trivial extraction of implicit, previously unknown
and potentially useful information from data
 Exploration & analysis, by automatic or
semi-automatic means, of large quantities of data
in order to discover meaningful patterns
 Typical DM tasks: Clustering, classification,
association rule mining
 Applications: wherever there is data:
 Business, World Wide Web (content, structure, usage),
Biology, Medicine, Astronomy, Social Networks, Elearning, Images, …, etc
Introduction
 Information overload: too much information to sift/browse
through in order to find desired information
 Most information on Web is actually irrelevant to a particular user
 This is what motivated interest in techniques for Web
personalization
 As they surf a website, users leave a wealth of historic data
about what pages they have viewed, choices they have made,
etc
 Web Usage Mining: A branch of Web Mining (itself a branch
of data mining) that aims to discover interesting patterns
from Web usage data (typically Web Access Log
data/clickstreams)
Classical Knowledge Discovery Process For
Web Usage Mining
 Source of Data: Web Clickstreams: that get recorded in
Web Log Files:
 Date, time, IP Address/Cookie, URL accessed, …etc
 Goal: Extract interesting user profiles: by categorizing
user sessions into groups or clusters
 Profile 1:
 URLs a, b, c
 Sessions in profile 1
 Profile 2:
 URLs x, y, z, w
 Sessions in profile 2
 …etc
Classical Knowledge Discovery Process For
Web Usage Mining
Complete KDD process:
Preprocessing:
selecting and
cleaning data
(result = session data)
Classical Knowledge Discovery Process For
Web Usage Mining
Complete KDD process:
Data Mining
(Learning Phase):
• Clustering algorithm to
categorize sessions into usage
Preprocessing:
categories/modes/profiles
selecting and
• Frequent Itemset Mining
cleaning data
algorithms to discover frequent
(result = session data) usage patterns/profiles
Classical Knowledge Discovery Process For
Web Usage Mining
Complete KDD process:
Data Mining
(Learning Phase):
Preprocessing:
selecting and cleaning
data
(result = session data )
Derivation
and Interpretation
of results:
• Computing profiles
• Clustering algorithm to
categorize sessions into usage • Evaluating results
• Analyzing profiles
categories/modes/profiles
• Frequent Itemset Mining
algorithms to discover frequent
usage patterns/profiles
Web Personalization
 Web Personalization: Aims to adapt the Website according to
the user’s activity or interests
 Intelligent Web Personalization: often relies on Web Usage
Mining (for user modeling)
 Recommender Systems: recommend items of interest to the
users depending on their interest
 Content-based filtering: recommend items similar to the items liked
by current user

No notion of community of users (specialize only to one user)
 Collaborative filtering: recommend items liked by “similar” users

Combine history of a community of users: explicit (ratings) or implicit
(clickstreams)
 Hybrids: combine above (and others)
Focus of our
research
Different Steps Of our Web Personalization
System
STEP 1: OFFLINE
PROFILE
DISCOVERY
Site Files
STEP 2: ACTIVE RECOMMENDATION
Post Processing /
Derivation of
User Profiles
Preprocessing
Server
Logs
Data Mining:
Transaction Clustering
Association Rule Discovery
User Sessions Pattern Discovery
Recommendation
Engine
User profiles/
User Model
Active Session Recommendations
Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE
PROFILE DISCOVERY
ACTIVE RECOMMENDATION
Post Processing /
Derivation of
User Profiles
User profiles/
Site Files
Preprocessing
Server
Logs
User Model
Recommendation
Engine
Active Session Recommendations
Data Mining:
Transaction Clustering
Association Rule Discovery
User Sessions Pattern Discovery
Dealing with Ambiguity: Semantics?
• Implicit taxonomy? (Nasraoui, Krishnapuram, Joshi. 1999)
•Website hierarchy (can help disambiguation, but limited)
• Explicit taxonomy? (Nasraoui, Soliman, Badia, 2005)
•From DB associated w/ dynamic URLs
•Content taxonomy or ontology (can help disambiguation, powerful)
• Concept hierarchy generalization / URL compression / concept
abstraction:
(Saka
Nasraoui,in2006)
Nasraoui: Web Usage
Mining&
& Personalization
and Ambiguous
Environments
•How Noisy,
doesDynamic,
abstraction
affect
quality of user models?
Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE
PROFILE DISCOVERY
ACTIVE RECOMMENDATION
Post Processing /
Derivation of
User Profiles
Site Files
Preprocessing
Server
Logs
User Sessions
Recommendation
Engine
User profiles/
User Model
Active Session Recommendations
Data Mining:
Transaction Clustering
Association Rule Discovery
Pattern Discovery
User Profile Post-processing Criteria?
(Saka & Nasraoui, 2006)
• Aggregated profiles (frequency average)?
• Robust profiles (discount noise data)?
• How do they really perform?
•How to validate? (Nasraoui & Goswami, SDM 2006)
Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE
PROFILE DISCOVERY
ACTIVE RECOMMENDATION
Post Processing /
Derivation of
User Profiles
Site Files
Preprocessing
Server
Logs
Recommendation
Engine
User profiles/
User Model
Active Session Recommendations
Data Mining:
Transaction Clustering
Association Rule Discovery
User Sessions Pattern Discovery
Evolution: (Nasraoui, Cerwinske, Rojas, Gonzalez. CIKM 2006)
Detecting & characterizing profile evolution & change?
Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE
PROFILE DISCOVERY
ACTIVE RECOMMENDATION
Post Processing /
Derivation of
User Profiles
Site Files
Preprocessing
Server
Logs
Recommendation
Engine
User profiles/
User Model
Active Session Recommendations
Data Mining:
Transaction Clustering
Association Rule Discovery
User Sessions Pattern Discovery
In case of massive evolving data streams:
•Need stream data mining (Nasraoui et al. ICDM’03, WebKDD 2003)
•Need stream-based recommender systems? (Nasraoui et al. CIKM 2006)
•How do stream-based recommender systems perform under evolution?
•How to validate above? (Nasraoui et al. CIKM 2006)
Clustering: HUNC Methodology
•Hierarchical Unsupervised Niche Clustering (HUNC) algorithm:
a robust genetic clustering approach.
Hierarchical: clusters the data recursively and discovers profiles at
increasing resolutions to allow finding even relatively small
profiles/user segments.
•
•
Unsupervised: determines the number of clusters automatically.
Niching: maintains a diverse population in GA with members
distributed among niches corresponding to multiple
solutions  smaller profiles can survive alongside bigger profiles.
•
Genetic optimization: evolves a population of candidate solutions
through generations of competition and reproduction until convergence
to one solution.
•
Hierarchical Unsupervised Niche
Clustering Algorithm (H-UNC):
Encode binary
session vectors
0
1
1
0
1
1
0
All data
Cluster 1
Perform UNC in
hierarchical
mode (HUNC)
Cluster 1.1
Cluster 2 …
… Cluster 1.N
Cluster K
Cluster K.1
…Cluster K.M
Unsupervised Niche Clustering
(UNC)
Evolutionary
Algorithm
Niching
Strategy
“Hill climbing”
performed in parallel
with the evolution for
estimating the niche
sizes accurately and
automatically
Unsupervised Niche Clustering
(UNC)
0
1
1
1
fi
0
1


N
0
w ij
j 1
2
i

2


d

ij 
wij  exp 
2

 2 i 

Role of Similarity Measure: Adding
Semantics
 we exploit both
 an implicit taxonomy as inferred from the website
directory structure (we get this by tokenizing URL),
 an explicit taxonomy as inferred from external data:
Relation: object 1 is parent of object 2 …etc
 Both implicit and explicit taxonomy
information are seamlessly incorporated into
clustering via a specialized Web session
similarity measure
Similarity Measure
• Map NU URLs on site to indices
• User session vector s(i) : temporally compact sequence of Web
accesses by a user
th
1 if user accessed j URL
s (ji )  
0 otherwise
If site structure ignored cosine similarity


NU
i 1
( k ) (l )
si si
S1, kl 
NU ( k )
NU ( l )
s
i 1 i
i 1 si
Taking site structure into account  relate distinct URLs



pi  p j

Su (i, j )  min 1,
 max 1, max pi , p j  1 


th


 
• pi: path from root to i URL’s node
Multi-faceted Web user profiles
Pre-processing
Web Logs
Server
content DB
Data Mining
about which
companies?
from which
companies?
Postprocessing
search
queries
viewed Web
pages?
Example: Web Usage Mining of January 05 Log
Data
Prof. 3
#users:
75
Prof. 4
#users:
455
Prof. 5
#users:
46
Prof. 8
#users:
323
Prof. 0
#users:
295
Prof. 12
#users:
21
Prof. 11
#users:
55
Prof. 10
#users:
56
Prof. 18
#users:
89
Prof. 19
#users:
128
Prof. 14
#users:
Prof. 7
308
#users:
152
Jan. 05
Web Logs
Prof. 17
#users:
188
Prof. 15
#users:
44
Prof. 6
#users:
51
Prof. 2
#users:
60
Prof. 1
#users:
88
Prof. 16
#users:
280
Prof.20
#users:
94
Jan. 05
Web Logs
Prof. 13
#users:
88
Prof. 9
#users:
94
Prof. 21
#users:
68
Prof. 25
#users:
105
Prof. 24
#users:
102
Total Number of Users: 3333
26 profiles
Prof. 23
#users:
42
Prof. 22
#users:
26
Web Usage Mining of January 05 Cont…
Who are these Users?
/FRAMES.ASPX/MENU_FILE=.JS&MAIN=/UNIVERSAL.ASPX
/TOP_FRAME.ASPX
Some are:
/LEFT1.HTM
What
web pages
/MAINPAGE_FRAMESET.ASPX/MENU_FILE=.JS&MAIN=/UNIVERSAL.ASPX
•Highway Customers/Austria Facility
/MENU.ASPX/MENU_FILE=.JS
Prof. 4
did
these users
Prof. 17
#users:
•Naval
Education
And Training Program
/UNIVERSAL.ASPX/ID=Company
/ Suppliers/Soda Blasting
Prof. 5
Prof. 3 Connection/Manufacturers
#users:
Prof.
16
Prof. 18
visit?
455
#users:
#users:
/UNIVERSAL.ASPX/ID= Surface Treatment/Surface Preparation/Abrasive Blasting Management/United
188 States
#users:
#users:
46
75
Prof. 6
#users:
51
Prof. 2
#users:
60
Prof. 1
What page did
#users:
they visit before88
starting their
Prof. 0
session? #users:
Prof. 7
#users:
152
Jan. 05
Web Logs
Prof. 8
#users:
323
295
Some are:
Prof. 12
#users:
http://www.eniro.se/query?q=www.nns.com&what
21
Prof. 11
Prof. 10
=se&hpp=&ax=
#users:
#users:
55
56
•
Prof. 9
#users:
94
280
89
•
Magna International Inc/Canada
Prof. 15
Prof. 19
#users:• Norfolk Naval Shipyard (NNSY).
#users:
44
128
Prof. 14
#users:
308
Prof.20
What did #users:
94
they search for
before visiting
Prof. 21
our website?#users:
68
Jan. 05
Web Logs
Prof. 13
#users:
88
Prof. 25
#users:
105
•http://www.mamma.com/Mamma?qtype=0&query
=storage+tanks+in+water+treatment+plant%2Bcor
rosion
•http://www.comcast.net/qry/websearch?cmd=qry
&safe=on&query=mesa+cathodic+protection&sear
chChoice=google
Total Number of Users:3333
•http://msxml.infospace.com/_1_2O1PUPH04B2YN
Prof. 24
#users:
102
Prof. 22
#users:
26
Prof. 23
Some
are:
#users:
•“shell42blasting”
•“Gavlon Industries
•“obrien paints”
•“Induron Coatings”
•“epoxy polyamide”
Two-Step Recommender Systems Based on a
Committee of Profile-Specific URL-Predictor
Neural Networks
Current Session
Two-Step Recommender Systems Based on a
Committee of Profile-Specific URL-Predictor
Neural Networks
Current Session
Step 1:
Find closest
Profile
discovered
by HUNC
Two-Step Recommender Systems Based on a
Committee of Profile-Specific URL-Predictor
Neural Networks
Step 2: Choose its
specialized network
NN #1
NN #2
Current Session
Step 1:
Find closest
profile
?
NN #3
….etc
NN # n
Two-Step Recommender Systems Based on a
Committee of Profile-Specific URL-Predictor
Neural Networks
Step 2: Choose the
specialized network
NN #1
NN #2
Current Session
Step 1:
Find closest
profile
?
NN #3
….etc
NN # n
Recommendations
Train each Neural Net to Complete missing puzzles
(sessions from that profile/cluster)
?
?
?
complete session
Striped = Input session
(incomplete) to the neural
network
“?” = Output that is
predicted to complete the
puzzle
Precision: Comparison 2-step, 1-step, & K-NN
(better for longer sessions)
Precision
1
0.9
2-step Profile-specific NN
0.8
0.7
K-NN(K=50,N=10)
Precision
0.6
0.5
1-step Decision Tree
0.4
1-step Nearest Profile-Cosine
1-step Nearest ProfileWebSession
1-step Neural Network
0.3
0.2
0.1
0
0
1
2
3
4
5
subsession size
6
7
8
9
10
Coverage: Comparison 2-step, 1-step, and & KNN (better for longer sessions)
Coverage
0.9
0.8
0.7
2-step Profile-specific NN
K-NN(K=50,N=10)
0.6
Coverage
1-step Decision Tree
0.5
1-step Neural Network
1-step Nearest Profile-Cosine
0.4
1-step Nearest Profile-WebSession
0.3
0.2
0.1
0
0
1
2
3
4
5
subsession size
6
7
8
9
10
E-Learning HyperManyMedia Resources
http://www.collegedegree.com/library/college-life/the_ultimate_guide_to_using_open_courseware
Architecture
Semantic representation ( knowledge representation),
2. Algorithms (core software), and
3. Personalization interface.
1.
Semantic Search Engine
Experimental Evaluation
A total of 1,406 lectures (documents) represented the profiles, with the size of each profile varying
from one learner to another, as follows. Learner1 (English)= 86 lectures, Learner2 (Consumer
and Family Sciences) = 74 lectures, Learner3 (Communication Disorders) = 160 lectures,
Learner4 (Engineering) = 210 lectures, Learner5 (Architecture and Manufacturing Sciences) =
119 lectures, Learner6 (Math) = 374 lectures, Learner7 (Social Work) = 86 lectures, Learner8
(Chemistry) = 58 lectures, Learner9 (Accounting) = 107 lectures, and Learner10 (History) = 132
lectures.
Concept drift / Data Streams
TECNO-Streams: inspired by the immune system
[ICDM 2003, WEBKDD 2003, COMPUTER
NETWORKS 2006]
CIKM results: collaborative filtering recommendation
in evolving streams
TRAC-Streams: extends a robust clustering algorithm
to streams [SDM 2006]
Recommender Systems in Dynamic Usage Environments
 For massive Data streams, must use a stream mining
framework
 Furthermore must be able to continuously mine evolving data
streams
 TECNO-Streams: Tracking Evolving Clusters in Noisy Streams
 Inspired by the immune system
 Immune system: interaction between external agents (antigens) and
immune memory (B-cells)
 Artificial immune system:







Antigens = data stream
B-cells = cluster/profile stream synopsis = evolving memory
B-cells have an age (since their creation)
Gradual forgetting of older B-cells
B-cells compete to survive by cloning multiple copies of themselves
Cloning is proportional to the B-cell stimulation
B-cell stimulation: defined as density criterion of data around a profile (this is
what is being optimized!)
Nasraoui: Web Usage Mining & Personalization in
O. Nasraoui, C. Cardona, C. Rojas, and F. Gonzalez. Mining Evolving User Profiles in Noisy Web Clickstream Data with a
Noisy, Dynamic, and Ambiguous Environments
Scalable Immune System Clustering Algorithm, in Proc. of WebKDD 2003, Washington DC, Aug. 2003, 71-81.
The Immune Network  Memory
External antigen
(RED) stimulates
binding B-cell  Bcell (GREEN)
clones copies of
itself (PINK)
Stimulation breeds
Survival
Even after
external antigen
disappears:
B-cells costimulate each
other  thus
sustaining each
other  Memory!
General Architecture of Proposed Approach
Evolving data
stream 
1-Pass Adaptive
Immune
Learning
Immune network
information system
?
Evolving Immune
Network
(compressed into
subnetworks)
Stimulation (competition
& memory)
Age (old vs. new)
Outliers (based on
activation)
Initialize ImmuNet and MaxLimit
Compress ImmuNet into K subNet’s
Trap Initial Data
Memory
Constraints
Present NEW antigen data
Identify nearest subNet*
Compute soft activations in subNet*
Update subNet* ‘s ARB Influence range /scale
S
Update subNet* ‘s ARBs’ stimulations
Yes
Activates
ImmuNet?
Start/
Reset
No
Clone antigen
Clone and Mutate ARBs
Domain
Knowledge
Constraints
Outlier?
Kill lethal ARBs
#ARBs >
MaxLimit?
ImmuNet
Stat’s &
Visualization
Yes
No
Compress ImmuNet
Kill extra ARBs (based on
age/stimulation strategy) OR
increase acuteness of competition OR
Move oldest patterns to aux. storage
Secondary
storage
Summarizing noisy data in 1 pass
 Different Levels of Noise
Validation for clustering streams in 1 pass:
- Detect all clusters ( miss no cluster)
- do not discover spurious clusters
 Validation measures (averaged over 10 runs):


Hits,
Spurious Clusters
Experimental Results
 Arbitrary Shaped Clusters
Experimental Results
 Arbitrary Shaped Clusters
Concept drift / Data Streams
TECNO-Streams: inspired by the immune system
[ICDM 2003, WEBKDD 2003, COMPUTER
NETWORKS 2006]
CIKM results: collaborative filtering recommendation
in evolving streams
TRAC-Streams: extends a robust clustering algorithm
to streams [SDM 2006]
Validation Methodology in Dynamic
Environments
Limit Working Capacity (memory) for Profile Synopsis in TECNOStreams (or Instance Base for K-NN) to 30 cells/instances
Perform 1 pass mining + validation
• First present all combination subset(s) of a real ground-truth session to
recommender,
• Determine closest neighborhood of profiles from TECNO-Stream’s synopsis (or
instances for KNN)
• Accumulate URLs in neighborhood
• Sort and select top N URLs  Recommendations
• Then Validate against ground-truth/complete session (precision, coverage, F1),
• Finally present complete session to TECNO-Streams (and K-NN)
Mild Changes: F1 versus session number, 1.7K sessions
TECNO-Streams higher
(noisy, naturally occurring
but unexpected fluctuation in
user access patterns)
Memory capacity limited to 30 nodes in TECNOStreams’ synopsis, 30 KNN-instances
Concept drift / Data Streams
TECNO-Streams: inspired by the immune system
[ICDM 2003, WEBKDD 2003, COMPUTER
NETWORKS 2006]
CIKM results: collaborative filtering recommendation
in evolving streams
TRAC-Streams: extends a robust clustering algorithm
to streams [SDM 2006]
TRAC-STREAMS:
Robust weights: decrease in distance & in time since data arrived
criterion ≈ density =  weights * distances/scale -  weights
Adaptive Robust Weight with gradual forgetting of Old patterns
Objective function:
First term: Robust sum of normalized errors
Second term: robust soft count of inliers/good points that are not noise
Total Effect: minimize robust error, while including as many non-noise points as
possible – To maximize robustness & efficiency from robust estimation point of
view
Details
- Incremental Updates: J/c = 0, J/ = 0,
- Centers & scale: updated with every new data point from the
stream
- Cluster parameters (c, ) slowly forget older data, and track new
data
-Chebyshev test:
-to determine whether a new data record is an outlier
-To determine whether two clusters should be merged
Incremental location (center) update:
Incremental scale update:
Results in 1 pass over noisy data stream:
- Number of clusters determined automatically
- Clusters vary in size and density
- Noisy data
- Scales are automatically estimated in 1 pass
P2P
 Information is exchanged in a decentralized manner
 Attractive platform for participants in contraband information
exchange due to the sense of “anonymity” that prevails while
performing P2P transactions.
 P2P networks vary in the level of preserving the anonymity of their
users.
 Information Exchange in a broadcast-based (unstructured) P2P
retrieval:





Query is initiated at a starting node,
then it is relayed from this node to all nodes in its neighborhood,
and so on…
until a matching information is found on a peer node,
Finally, a direct communication is established from the initiating node and
the last node to transfer the content.
 As query propagates on its way, each node does not know whether:


Neighboring node is asking for info for itself
Or simply transmitting/broadcasting another node’s query
  Anonymity  Illegal exchanges: e.g. child pornography material
Possible Roles of Nodes
Creator/Top Level
Distributor (A)
Connection (B)
Recipient (C)
Connection (B)
Recipient
(C)
Connection (B)
Recipient (C)
Steps in Mining P2P Networks
 Undercover Node-based Probing and Monitoring: (next
slide)
 to Build an Approximate Model of Network Activity
 Flagging Contraband Content:
 key word,
 hashes,
 other patterns
 Evaluation against different scenarios:
 recipient querying,
 distribution and
 routing cases
 Using the Evaluation results to fine-tune the node
positioning strategy
Normal or
Undercover
Recipient
(initiates query)
query
query
query
query
query
found
X
query
X
query
query
query
found
query
undercover
query
Undercover
(Y)
(Y)
query
query
query
found
query
Z
Z
found
query
Distributor
Reconstructing Illegal Exchanges
 Each undercover node  capture a local part of the exchange
 All nodes  “collective” information  attempt to reconstruct a bigger
picture of the exchange
 Infer most likely
 contraband query initiators (or recipients, thus type C) and
 content distributors (thus type A)
 ==> Suspect Rankings of the nodes
 How to perform the above “inference”?
 relational probabilistic inference , or
 network influence propagation mechanisms


where evidence is gradually propagated in several iterations via the links from node to
node.
Belief Propagation algorithms that rely on message passing from each node to its
neighbors
 Challenges:
 Keeping up to date with the dynamic nature of the network (as new nodes
leave and new nodes join)
 Aim for a close (yet approximate) snapshot to reality in a statistical sense,

particularly using a large number of concurrent crawlers in parallel.
Undercover Node-based Probing:
Positioning nodes…
 Distributed crawling of P2P networks to: collect
 logical topology (i.e. virtual connections between nodes that determine
who are the neighbors accessible to each node when passing a message)
 network topology (i.e. actual network identifiers such as IP address of a
node)
 Analysis of correlations between the logical and network topologies
 by treating network topologies (e.g. top domain) as class information,
 by clustering the logical topology,
 and then comparing the class versus cluster entropies
 using for e.g. the information gain measure.
 Using the above results to validate assumptions about the relationships
between logical and network topologies.
 Using the results to refine node positioning in our proposed Node-based
Probing and Monitoring algorithm in the next step
Olfa Nasraoui
Computer Engineering & Computer Science
University of Louisville
Olfa.nasraoui@louisville.edu
In collaboration with
Joan Schmelz
Department of Physics
University of Memphis
jschmelz@memphis.edu
Acknowledgement: team members who worked on this project:
Nurcan Durak, Sofiane Sellah, Heba Elgazzar, Carlos Rojas (Univ. of
Louisville)
Jonatan Gomez and Fabio Gonzalez (National Univ. of Colombia)
Jennifer Roames, Kaouther Nasraoui (Univ. of Memphis)
NASA-AISRP PI Meeting, Univ. Maryland,
Oct. 3-5 2006
Nasraoui & Schmelz: Mining Solar Images to Support
Astrophysics Research
Motivations (1): The Coronal Heating Problem
 The question of why the solar corona is so hot remains one of the
most exciting astronomy puzzles for the last 60 years.
 Temperature increases very steeply


from 6000 degrees in photosphere (visible surface of the Sun)
to a few million degrees in the corona (region 500 kilometers above the
photosphere).
 Even though the Sun is hotter on the inside than it is on the outside.
 The outer atmosphere of the Sun (the corona) is indeed hotter than the
underlying photosphere!
 Measurements of the temperature distribution along the coronal
loop length can be used to support or eliminate various classes of
coronal temperature models.
 Scientific analysis requires data observed by instruments such as
EIT, TRACE, and SXT.
Motivations (2): Finding Needles in Haystacks (manually)
 The biggest obstacle to completing the coronal temperature
analysis task is collecting the right data (manually).
 The search for interesting images (with coronal loops) is by far the most
time consuming aspect of this coronal temperature analysis.
 Currently, this process is performed manually.
 It is therefore extremely tedious, and hinders the progress of science in
this field.
 The next generation "EIT" called MAGRITE, scheduled for
launch in a few years on NASA's Solar Dynamics Observatory,
should be able to take
 as many images in about four days
 as was taken by EIT over 6 years!
 and will no doubt need state of the art techniques to sift through the
massive data to support scientific discoveries
Goals of the project: Finding Needles in Haystacks
(automatically)
 Develop an image retrieval system based on Data
Mining
 to quickly sift through data sets downloaded from online
solar image databases
 and automatically discover the rare but interesting images
containing solar loops, which are essential in studies of the
Coronal Heating Problem
 Publishing mined knowledge on the web in an easily
exchangeable format for astronomers.
Sources of Data
 EIT: Extreme UV Imaging Telescope aboard the
NASA/European Space Agency spacecraft called SOHO
(Solar and Heliospheric Observatory)
http://umbra.nascom.nasa.gov/eit
 TRACE: NASA’s Transition Region And Coronal
Explorer
http://vestige/lmsal.com/TRACE.SXT
 SXT: Soft X-ray Telescope database on the Japanese
spacecraft Yohkoh:
http://ydac.mssl.ucl.ac.uk/ydac/sxt/sfm-cal-top.html
Samples of Data: EIT
NASA-AISRP PI Meeting,
Univ. Maryland, Oct. 3-5 2006
NASA-AISRP PI Meeting, Univ.
Maryland, Oct. 3-5 2006
NASA-AISRP PI Meeting, Univ.
Maryland, Oct. 3-5 2006
Steps
Sample Image Acquisition and Labeling:
1.
images with and without solar loops, 1020 X 1022 ~ 2 MB / image

Image Preprocessing, Block Extraction, and Feature
extraction
Building & Evaluating Classification Models
2.
3.

At block level (is a block a loop or no-loop block?)




10-fold cross validation
Train, then test on independent set 10 times,
average results
At image level (does an image contain a loop block?)

Use model learned from training data


One global model, or 1 model/solar cycle
Test on independent set of images from different solar cycles
1.
Step 2. Image Preprocessing and Block
Extraction
 Despeckling (to clean noise) and Gradient
Transformation (to bring out the edges)
 Phase I (loops out of solar disk): divide
area outside solar disk into blocks with an
optimal size (to maximize overlap with
marked areas over all training images)
 Use each block as one data record to
extract individual data attributes for
learning and testing
Difficult classification problem
Loops come in different sizes, shapes, intensities,
…etc
Hardly distinguishable regions without
interesting loops
 Inconsistencies in labeling are common
 Subjectivity, quality of data
Even at edge level: challenging
 Which block is NOT a loop block?
Even at edge level: challenging
 Which block is NOT a loop block?
Defective and Asymmetric nature of Loop
Shapes
Features inside each block - applied on the original
intensity levels
 Statistical Features
 Mean
 Standard Deviation
 Smoothness
 Third Moment
 Uniformity
 Entropy
Features inside each block - applied on edges
 Hough-based Features
First apply Hough transform





Image space  Hough Space (H.S.)
Pixel  parameter combination for a given shape
All pixels  vote for several parameter combinations
Extract peaks from H.S.
Then construct features based on H.S.

 Peak detection is very challenging:



Many false peaks (noise)
Bin splitting (peaks are split)
Biggest problem: size of Hough accumulator array:



Every pixel votes for all possible curves that go trough this pixel
Combinatorial explosion as we add more parameters
Solution: we feed the Hough space into a stream clustering
algorithm to detect peaks
TRAC-Stream clustering
eliminate need to “store” Hough accumulator array by processing it in 1 pass
 Input: initial scales 0, max. No. of clusters
 Output: running (real-time) synopsis of clusters in input stream
 Repeat until end of stream {
 Input next data point x
 For each cluster in current synopsis {


Perform Chebyshev test (test for compatibility without any assumptions on distributions, but
requires robust scale estimates)
If x passes Chebyshev test Then

Update cluster parameters: centroid, scale
 }
 If no cluster or x fails all Chebyshev tests Then

Create new cluster (c=x, 0
 Perform pairwise Chebyshev tests to merge compatible clusters:


densest cluster absorbs merged cluster
Centroid updated
 Eliminate clusters with low density
 }
Examples of clustering 2-D Hough space
Curvature Features
Original Image
Automatically
Detected Curves
Step 3. Classification
Classification algorithm
Type of classifier
Decision Stump
Decision Tree Induction & Pruning
C4.5
Decision Tree Induction
Adaboost
Decision Tree w/ Boosting
RepTree
Tree Induction
Conjunctive Rule
Rule Generation
Decision Table
Rule Generation
PART
Rule Generation
JRip
Rule Generation
1-NN
Lazy
3-NN
Lazy
SVM (Support Vector Machines)
Function based
Multi-Layer Perceptron (Neural Network)
Function based
Naive Bayes (Bayesian classifier)
Probabilistic
Mont
Total
h
Viewed
Images
Aug.
96
Mar.
00
Dec.
00
Feb.
04
Jan.
05
Jun.
05
Jun.
96
Dec.
96
Loop
Imag
e
s
# Loops on
limb
# Loops
on
disk
Wavelength
Ratio of limb
loops / viewed
imgs
Ratio of limb
loops /
All loops:
3/37 = 8% 3/6=50%
37
6
3
3
171
1/115= 0.8% 1/6=16%
115
6
1
5
171
3/123= 2% 3/18=1/6=16%
123
18
3
15
171
1/111= 0.9% 1/17=5%
111
372
360
17
48
18
1
15
17
16
171
15/372= 4% 15/48=31%
33
171,195,2
84,304
17/360= 4% 17/18=94%
13
171,195,2
84,304
2/113= 1% 2/14=14%
113
14
2
12
171
23/167= 13% 23/162=14%
167
162
Mar.NASA-AISRP PI Meeting, Univ.
Maryland, Oct. 3-5 2006
97
154
31
23
139
171
Nasraoui & Schmelz: Mining Solar Images to Support
3
171
Astrophysics
Research28
3/154= 2% 3/31=9%
Block-based results
Features / Statistical
Classifier
Houghbased
Pre. Rec. Pre. Rec.
Spatial
Pre. Rec.
Curvature
All Features
Pre
Pre.
Rec.
AdaBoost
0.39
0.256
0.464
0.4
0.458
0.409
0.416
0.325
NB
0.193
0.057
0.398
0.618
0.437
0.501
0.289
0.801
MLP
C4.5
0.484
0.218
0.463
0.457
0.463
0.139
0.442
0.3
0.459
0.266
0.441
0.362
0.481
0.434
0.419
0.218
RIPPER
K-NN
(k=5)
0.515
0.166
0.487
0.325
0.498
0.36
0.448
0.431
0.201
0.472
0.357
0.479
0.31
0.377
Rec.
0.643
0.596
0.404
0.573
0.6
0.638
0.548
0.536
0.233
0.591
0.613
0.208
0.641
0.462
150 solar images from 1996, 1997, 2000, 2001, 2004 2005
403 Loop blocks
7950 No-loop blocks
Loop Mining Tool
Nasraoui & Schmelz: Mining Solar Images to Support
Astrophysics Research
Image Based Testing Results
Actual Loop Images
Predicted
Loop Images
No-Loop Images
Total
Minimum
Cycle
Pre.
Rec.
0.5
0.9
40
11
51
No-Loop Images
Total
10
39
49
50
50
100
Medium
Cycle
Maximum All Cycles
Cycle
Pre. Rec.
Pre. Rec. Pre. Rec.
0.8
0.7
0.8
1
0.88
0.83
• More DATA  Data MINING
• FASTER Data Arrival Rates (Massive Data)  STREAM Data
Mining
• Many BENEFITS:
• from DATA to KNOWLEDGE!!!
• Better KNOWLEDGE  better DECISIONS  help society: E.g. Scientists,
Businesses, Students, Casual Web Surfer, Cancer Patients, Children…
• Many RISKS: Privacy, Ethics, Legal Implications:
• Might anything that we do harm certain people?
•
Who will use a certain Data Mining tool?
•
•
What if certain Govts. use it to catch political dissidents?
Who will benefit from DM?
•
FAIRNESS: E.g. shouldn’t businesses/websites reward Web surfers in return for the wealth of
data/user profiles that their clickstreams provide???
•Questions/Comments/Discussions????