Transcript data7 lecture - NDSU Computer Science

Vertical Data 3
The DataMIME™ System
(DataMIMEtm = Data Mining, NO NOISE)
YOUR DATA MINING
YOUR DATA
Data Integration Language
Ptree (Predicates) Query Language
DIL
PQL
Internet
DII (Data Integration Interface)
DMI (Data Mining Interface)
Data Repository
lossless, compressed, distributed, verticallystructured database
Generalized Raster and Peano Sorting: generalizes to any table with
numeric attributes (not just images).
Raster Sorting:
Peano Sorting:
Decimal
Binary
Unsorted relation
Attributes 1st
Bit position 1st
Bit position 2nd
Attributes 2nd
Generalize Peano Sorting
KNN speed improvement
(using 5 UCI Machine Learning Repository data sets)
Time in Seconds
120
100
80
60
40
20
0
Unsorted
Generalized Raster
Generalized Peano
Astronomy Application:
(National Virtual Observatory data)
What Ptree dimension and ordering should be used for astronomical data?, where all bodies are assumed to
lie on the surface of a celestial sphere (shares its origin and equatorial plane with earth but has no
specified radius)
Hierarchical Triangle Mesh Tree (HTM-tree, seems to be an accepted standard)
Peano Triangle Mesh Tree (PTM-tree) is a [better?] alternative - at least for data mining?
(Note: RA=Recession Angle (=longitudinal angle); dec=declination (=latitudinal angle)
PTM is similar to HTM used in the Sloan Digital Sky Survey project (which is a project to create a National
Virtual Observatory of all [?] telescope data integrated into one repository).
In both:
 The Celestial Sphere is divided into triangles with great circle segment sides.
 But PTM differs from HTM in the way in which these triangles are ordered at each level.
The difference between HTM and
PTM-trees is in the ordering.
1,3,3
1,1,2
1,3,1
1.1.3
1,1,1
1
1,2
1
1,3,0
1,1,0
1,
21,1
1,3
1,0
1,1
Ordering of HTM
Why use a different ordering?
1,
0
1,
3
Ordering of PTM-tree
1,3,2
PTM Triangulation of the Celestial Sphere
The following ordering produces a sphere-surface filling curve with good continuity characteristics,
The picture at right shows the earth (blue ball at the center) and the celestial sphere out around it.
Traverse
southern
Next, traverse
the
hemisphere
in
the
southern hemisphere
revere
direction
in the revere
direction
(just
the
identical
(just
the identical
pattern pushed
pattern
pushed down
down instead
of
instead
of pulled
up,
pulled up,
arriving
Equilateral triangle
(90o
arriving
at the
at the
sector) bounded
by Southern
of the of
Southern
longitudinal neighbor
and equatorialneighbor
line segments
start
point.
the start point.
left
right
right
dec
left turn
RA
Traverse the next level of
triangulation, alternating
again with left-turn, rightturn, left-turn, right-turn..
PTM-triangulation - Next Level
LRLR RLRL LRLR
RLRL LRLR RLRL
LRLR RLRL
LRLR RLRL LRLR
RLRL LRLR RLRL
LRLR RLRL
Peano Celestial Coordinates
Unlike PTM-trees which initially partition the sphere into the 8 faces of an octahedron, in the PCCtree scheme: The
Sphere is tranformed to a cylinder, then into a rectangle, then standard Peano ordering is used on the Celestial Coordinates.
 Celestial Coordinates Recession Angle (RA) runs from 0 to 360 o dand Declination Angle (dec) runs from -90o to 90o.
90o
0o
South Plane
-90o
0o
360o
Sphere  Cylinder
 Plane
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PUBLIC (Ptree Unfied BioLogical
InformtiCs Data Cube and
Dimension Tables)
Organism
Species
Vert
Genome Size
(million bp)
human
Homo sapiens
1
3000
fly
Drosophila
melanogaster
0
185
yeast
Saccharomyces
cerevisiae
0
12.1
Mus
musculus
1
mouse
SubCell-Location
Myta
Ribo
Nucl
Ribo
Function
apop
meio
mito
apop
StopCodonDensity
.1
.1
.1
.9
PolyA-Tail
1
1
0
0
Organism
Dimension
Table
g0
g2
o0
1
1
0
0
e1
1
1
1
1
e0
0
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e2
1
1
1
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e2
P
I
U
N
V
S
T
R
C
T
Y
S
T
Z
E
D
A
D
S
H
M
N
1
e3
Experiment
1 Dimension
Table
3
2
a
c
h
2
2
b
s
h
2
4
a
c
a
1
2
4
a
s
a
1
0
(MIAME)
0
1
0
(chromosome,length)
g3
17, 78 12, 60 Mi, 40
1 0
1
10,
75 1 0
o1
1 e14,
0
0
65 0
0
0
o2 16, 760 0
1 9, 45 0 1, 43
o3
3000
g1
e1
L
A
B
Gene-Organism
Dimension Table
Gene Dimension Table
1, 48
7, 40 1
0 1
0
1
1
1
0
0
0
0
0
1
0
1
0
0
1
0
1
Gene-Experiment-Organism Cube
(1 iff that gene from that organism
expresses at a threshold level in that
experiment.)
many-to-many-to-many relationship
Association of Computing Machinery KDD-Cup-02
http://www.biostat.wisc.edu/~craven/kddcup/winners.html
BIOINFORMATICS Task: Yeast Gene Regulation Prediction

There are now experimental methods that allow biologists to measure some aspect of cellular "activity"
for thousands of genes or proteins at a time. A key problem that often arises in such experiments is in
interpreting or annotating these thousands of measurements. This KDD Cup task focused on using data
mining methods to capture the regularities of genes that are characterized by similar activity in a given
high-throughput experiment. To facilitate objective evaluation, this task did not involve experiment
interpretation or annotation directly, but instead it involved devising models that, when trained to
classify the measurements of some instances (i.e. genes), can accurately predict the response of held
aside test instances.

The training and test data came from recent experiments with a set of
S. cerevisiae (yeast) strains in which each strain is characterized by a single gene being knocked out.
Each instance in the data set represents a single gene, and the target value for an instance is a
discretized measurement of how active some (hidden) system in the cell is when this gene is knocked
out. The goal of the task is to learn a model that can accurately predict these discretized values. Such a
model would be helpful in understanding how various genes are related to the hidden system.

The best overall score (Kowalczyk) was 1.3217 (summed AROC for the two partitions). The best
score for the "narrow" partition was 0.6837 (Denecke et al), and the best score for the
"broad" partition was 0.6781 (Amal Perera, Bill Jockheck, Willy Valdivia Granda, Anne Denton,
Pratap Kotala and William Perrizo, North Dakota State University KDD Cup Page
http://www.acm.org/sigkdd/explorations/
Association of Computing Machinery KDD-Cup-02
http://www.biostat.wisc.edu/~craven/kddcup/winners.html
My Team
Association of Computing Machinery KDD-Cup-06
http://www.cs.unm.edu/kdd_cup_2006 http://www.cs.ndsu.nodak.edu/~datasurg/kddcup06/kdd6News.html
MEDICAL INFORMATICS Task:
Computer Aided Detection of Pulmonary Embolism
Description of CAD systems:
Over the last decade, Computer-Aided Detection (CAD) systems have moved from the sole realm of academic publications, to robust
commercial systems that are used by physicians in their clinical practice to help detect early cancer from medical images. For
example, CAD systems have been employed to automatically detect (potentially cancerous) breast masses and calcifications in Xray images, detect lung nodules in lung CT (computed tomography) images, and detect polyps in colon CT images to name a few
CAD applications. CAD applications lead to very interesting data mining problems. Typical CAD training data sets are large and
extremely unbalanced between positive and negative classes. Often, fewer than 1% of the examples are true positives. When
searching for descriptive features that can characterize the target medical structures, researchers often deploy a large set of
experimental features, which consequently introduces irrelevant and redundant features. Labeling is often noisy as labels are
created by expert physicians, in many cases without corresponding ground truth from biopsies or other independent
confirmations. In order to achieve clinical acceptance, CAD systems have to meet extremely high performance thresholds to
provide value to physicians in their day-to-day practice. Finally, in order to be sold commercially (at least in the United States),
most CAD systems have to undergo a clinical trial (in almost exactly the same way as a new drug would). Typically, the CAD
system must demonstrate a statistically significant improvement in clinical performance, when used, for example, by community
physicians (without any special knowledge of machine learning) on as yet unseen cases – i.e., the sensitivity of physicians with CAD
must be (significantly) above their performance without CAD, and without a corresponding marked increase in false positives
(which may lead to unnecessary biopsies or expensive tests). In summary, very challenging machine learning and data mining
tasks have arisen from CAD systems
Association of Computing Machinery KDD-Cup-06
http://www.cs.unm.edu/kdd_cup_2006
http://www.cs.ndsu.nodak.edu/~datasurg/kddcup06/kdd6News.html
Challenge of Pulmonary Emboli Detection: Pulmonary embolism (PE) is a condition that occurs when an artery in the lung
becomes blocked. In most cases, the blockage is caused by one or more blood clots that travel to the lungs from another part of
your body. While PE is not always fatal, it is nevertheless the third most common cause of death in the US, with at least 650,000
cases occurring annually.1 The clinical challenge, particularly in an Emergency Room scenario, is to correctly diagnose patients
that have a PE, and then send them on to therapy. This, however, is not easy, as the primary symptom of PE is dysapnea (shortness
of breath), which has a variety of causes, some of which are relatively benign, making it hard to separate out the critically ill
patients suffering from PE. The two crucial clinical challenges for a physician, therefore, are to diagnose whether a patient is
suffering from PE and to identify the location of the PE. Computed Tomography Angiography (CTA) has emerged as an accurate
diagnostic tool for PE. However, each CTA study consists of hundreds of images, each representing one slice of the lung. Manual
reading of these slices is laborious, time consuming and complicated by various PE look-alikes (false positives) including
respiratory motion artifacts, flowrelated artifacts, streak artifacts, partial volume artifacts, stair step artifacts, lymph nodes, and
vascular bifurcation, among many others. Additionally, when PE is diagnosed, medications are given to prevent further clots, but
these medications can sometimes lead to subsequent hemorrhage and bleeding since the patient must stay on them for a number of
weeks after the diagnosis. Thus, the physician must review each CAD output carefully for correctness in order to prevent
overdiagnosis. Because of this, the CAD system must provide only a small number of false positives per patient scan.
CAD system Goal: To automatically identify PE’s. In an almost universal paradigm for CAD algorithms, this problem
is addressed by a 3 stage system:
1. Identification of candidate regions of interest (ROI) from a medical image,
2. Computation of descriptive features for each candidate, and
3. Classification of each candidate (in this case, whether it is a PE or not) based on its features.
NPV Task: One of the most useful applications for CAD would be a system with very high (100%?) Negative
Predictive Value. In other words, if the CAD system had zero positive candidates for a given patient, we would
like to be very confident that the patient was indeed free from PE’s. In a very real sense, this would be the “Holy
Grail” of a PE CAD system.
The best NPV score was by Amal Perera, William Perrizo, North Dakota State University (twice as high as the next best
score!) http://www.acm.org/sigs/sigkdd/explorations/issue.php?volume=8&issue=2&year=2006&month=12
Association of Computing Machinery KDD-Cup-06
Professor William Perrizo and his PhD student Amal Shehan Perera of the department of computer science at North Dakota
State University (NDSU) won the KDD-Cup 2006 Knowledge Discovery and Data Mining competition which was held in
conjunction with the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The ACM
KDD-Cup is the most rigorous annual competition in the field of data mining and machine learning. The competition is
open to all academic institutes, industries as well as individuals from around the world. Since its inception in 1997, the
KDD-Cup competition has presented practical and challenging data mining problems. Considerable number of researchers
and practitioners participate in this annual contest. KDD-Cup datasets have become benchmarks for data mining research
over the years. KDD-Cup 2006 was conducted between May and August 2006 by the Association for Computing
Machinery(ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This year’s contest was
for a Computer-Aided Detection (CAD) system that could identify pulmonary embolisms, or blood clots, in the lung
through examinations of the features from Computed Tomography (CT) images. A typical CT study consists of hundreds of
images, each representing one slice of the lung. Manual reading of these slices is laborious, time consuming and
complicated. It is also very important to be accurate in the prediction. NDSU team won the Negative Predictive Value
(NPV) task of the competition, which was characterized by the organizers as the "Holy Grail" of Computer Aided
Detection (CAD) of pulmonary embolisms.
Siemens Medical Solutions provided dataset for the contest. Over 200 teams from around the
world registered for the competition and 65 entries were submitted. This year's tasks were
particularly challenging due to multiple instance learning, nonlinear cost functions, skewed class
distributions, noisy class labels, and sparse data space. The NDSU team used a combined
nearest neighbor and boundary classification with genetic algorithm parameter optimization. Dr.
William Perrizo is a senior Professor in Computer Science at the North Dakota State University.
He leads the Data Systems Users and Research Group (DataSURG) involved in innovative
research on scalable data mining research using vertical data structures in the Computer Science
Department at NDSU. DataSURG has been supported by NSF, NASA, DARPA, and GSA. Amal
Shehan Perera is a lecturer at the Department of Computer Science and Engineering at the
University of Moratuwa, Sri Lanka on study leave where he completed his PhD at NDSU.
Thank
you.