Automated Extraction and Parameterization of Motions in Large Data Sets
Extraction of high-level features from scientific data sets
description
Transcript of Extraction of high-level features from scientific data sets
Extraction of high-level features from scientific data sets
Eui-Hong (Sam) HanDepartment of Computer Science and Engineering
University of Minnesota
Research Supported by NSF, DOE, Army Research Office, AHPCRC/ARL
http://www.cs.umn.edu/~han
Joint Work with George Karypis, Ravi Jarnadan, Vipin Kumar, M. Pino Martin, Ivan Marusic, and Graham
Candler
Scientific Data Sets Large amount of raw data available
from scientific domains direct numerical simulations NASA satellite observations/climate data genomics astronomy
How do we apply existing data mining techniques on these data sets?
Direct Numerical Simulation
El Nino Effects on the Biosphere
C Potter and S. Klooster, NASA Ames Research Center
C4.5 Decision Trees
Tid Refund MaritalStatus
TaxableIncome Cheat
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes10
categorical
categorical
continuous
class
Refund
MarSt
TaxInc
YESNO
NO
NO
Yes No
Married Single, Divorced
< 80K > 80K
Splitting Attribute
The splitting attribute is determined based on the Gini index or Entropy gain
Associations in Transaction Data Sets
Dependency relations among collection of items appearing in transactions.
TID Items1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
5 Coke, Diaper, Milk
Frequent Item Sets: set of items that appear frequently together in transactions
|{Diaper, Milk}| = 3 |{Diaper,Milk,Beer}| = 2
Association Rules
Application Areas Inventory/Shelf planning Marketing and Promotion
}{},{ BeerMilkDiaper
%66|},{|
|},,{|
%20||
|},,{|
MilkDiaperBeerMilkDiaperconfidence
TBeerMilkDiapersupport
Challenges of Applying Data Mining Techniques
How do we construct transactions? in the presence of spatial attributes in the presence of temporal attributes
What are “interesting’’ events in the transactions? high level objects (e.g., vortex in simulation) high level features (e.g., El Nino event in
weather data) How do we find knowledge from the
transactions and interesting events?
Feature extraction from simulation data using decision trees
3-D isosurface of “swirl strength” Velocity normal to the
wall on XY plane (at z=30)
Which features are important for high upward velocityon the XY plane?
Transaction construction Given 3D swirl strength data and corresponding velocity data on the
XY plane at each simulation time step. swirl_strength(x,y,z) = 1 iff swirl strength at (x,y,z) > swirl threshold velocity(x,y) = 1 iff upward velocity at (x,y) > velocity threshold velocity(x,y) = -1 iff downward velocity at (x,y) > velocity threshold
A transaction corresponds to a grid point on the XY plane at one time step.
Class is velocity of the grid point Attributes correspond to swirl_strength(x,y,z) of the neighbors of the
point
x
yGrid point z
ss(-1:1,2:3,4:7)
C4.5 results on the simulation data
Given simulation data of 1000 time points first 500 time points were used for training set second 500 time points were used for testing set 10% sample of class 0 transactions
95% classification accuracy Recall/precision of 0.83/0.95 for class -1 and
0.67/0.93 for class 1Classified as
-1Classified as
0Classified as
1Class -1 6038 1220Class 0 320 125853 807Class 1 5129 10545
Discovered Rules & Features (F1:ss(0,1,0) = 0 & ss(-1,-2:-3,-4:-7) = 1 & ss(-1:1,-2:-3,8:15) = 1 & ss(1,0,2:3) = 1) => class 1 (F2: ss(0,1,0) = 0 & ss(-1:1,-2:-3,-4:-7) = 0 & ss(1,-1,-2:-3) = 0 & ss(2:3,2:3,-16:-31) = 0 & ss(1:0:-1) = 0) => class 0 (F3: ss(0,1,0) = 0 & …. & ss(-2:-3,2:3,8:15) = 1) => class -1
F1 => class 1
How to use the discovered features?
Finding association rules (F1, Vortex Type A) => (high energy, F5)
Finding sequential patterns (F2, Vortex Type A) => (F3, Vortex Type
B) => (class 1) Finding clusters of upward velocity
points based on discovered features, vortex types, and other variables.
Finding functional relationships
http://www.cgd.ucar.edu/stats/web.book/index.html
Regression techniques find global and/or contiguous relationships Association rules find local relationships with sufficient support
Need to find global relationships that have sufficient support
Finding functional relationships using duality transformation
Duality transformation in 2D space Point p=(a,b) => line p’ : y=ax-b Line l: y=Ax-B => point l’=(A,B) p on l => l’ on p’ l=line between p and q => l’ = intersection of p’ and q’
a
cb
d (1,-1)
a
cb
d y=x+1
Original space Transformed space Solution in the original space
Finding functional relationships using duality transformation
Given n points in d dimension, find all hyperplanes that have at least k number of data points on the hyperplane.
In the transformed space, given n hyperplanes in d dimension, find all the intersection points that have at least k hyperplanes.
Efficient algorithms to find intersections exist. These intersections corresponds to the
hyperplanes in the original space.
Functional relationships in synthetic data sets
1054 data points and 2000 noise points
Found all the intersections of two points in the transformed space
Drew a slope-sensitive grid on the transformed space
Selected grids that have above threshold intersection points
Plotted the average corresponding line of each selected grid on the original point space
Functional relationships in Ozone study
Case Studies in Environmental Statistics, by D. Nychka, W. Piegorsch, and L. Cox (http://www.cgd.ucar.edu/stats/web.book/index.html)
daily maximum ozone measurement as parts per million (ppm), temperature, wind speed, etc from 04/01/81 to 10/31/91 over Chicago area
found the most dominant functional relationship
wspd = 0.09*ozone - 0.14*temp + 2.9
Functional relationships in Ozone study
Found a less dominant functional relationshipwspd = 0.5*ozone - 0.4*temp +
3.03
This functional relationship covers only subset of data points on the lower levels of ozone measurement
Potential follow up studies what is unique about this
functional relationship? is there any unique
characteristics of the supporting set?
How to use discovered functional relationships?
Discover decision rules using both functional relationships and original variables. (supporting R1) and (Humidity > 80%) => class
high-ozone-level Discover association rules and sequential
patterns with these functional relationships ((supporting R2), Vortex Type A) => (high
upward velocity) Comparative analysis of supporting sets of
R1 and R2.
Research Issues in Finding Functional Relationships
Non-linear relationships can be found by introducing extra variables like x^2, sin(x), exp(x) for every variable x.
Spatial relationships can be found by introducing variables of neighbors.
Temporal relationships can also be found by associating time stamp with variables.
4.532 )1,1(2
)1,1(1
)0,0(
ttt zyx
Research Issues in Finding Functional Relationships
High computational cost of O(n^d) where n is the number of data points and d is the number of variables in the relationships.
Approximation algorithms are needed. Clustering data points to reduce n Focusing methods where inexact solutions are
found using faster algorithms and more accurate relationships are found focusing on these inexact solutions.
Iterative methods where the most dominant relationship is found first and less dominant relationships are found in the later iterations