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Data Mining using Fractals and Power laws. Christos Faloutsos Carnegie Mellon University. Thank you!. Prof. Hsing-Kuo Kenneth PAO Prof. Yuh-Jye LEE Hsin Yeh. And also thanks to. Lei LI Leman AKOGLU Ian ROLEWICZ. Ching-Hao (Eric) MAO Ming-Kung (Morgan) SUN Yi-Ren (Ian) YEH. Overview. - PowerPoint PPT Presentation
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School of Computer ScienceCarnegie Mellon
Data Mining using Fractals and Power laws
Christos Faloutsos
Carnegie Mellon University
ICAST, June'09 C. Faloutsos 2
School of Computer ScienceCarnegie Mellon
Thank you!
• Prof. Hsing-Kuo Kenneth PAO
• Prof. Yuh-Jye LEE
• Hsin Yeh
ICAST, June'09 C. Faloutsos 3
School of Computer ScienceCarnegie Mellon
And also thanks to
Ching-Hao (Eric) MAO
Ming-Kung (Morgan) SUN
Yi-Ren (Ian) YEH
Lei LI
Leman AKOGLU
Ian ROLEWICZ
ICAST, June'09 C. Faloutsos 4
School of Computer ScienceCarnegie Mellon
Overview
• Goals/ motivation: find patterns in large datasets:– (A) Sensor data– (B) network intrusion data
• Solutions: self-similarity and power laws
• Discussion
ICAST, June'09 C. Faloutsos 5
School of Computer ScienceCarnegie Mellon
Applications of sensors/streams
• network monitoring
time
# alerts
ICAST, June'09 C. Faloutsos 6
School of Computer ScienceCarnegie Mellon
Applications of sensors/streams
• Financial, sales, economic series
• Medical: ECGs +; blood pressure etc monitoring
ICAST, June'09 C. Faloutsos 7
School of Computer ScienceCarnegie Mellon
Motivation - Applications• Scientific data: seismological;
astronomical; environment / anti-pollution; meteorological
Sunspot Data
0
50
100
150
200
250
300
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School of Computer ScienceCarnegie Mellon
Motivation - Applications (cont’d)
• Computer systems
– web servers (buffering, prefetching)
– ...
http://repository.cs.vt.edu/lbl-conn-7.tar.Z
ICAST, June'09 C. Faloutsos 9
School of Computer ScienceCarnegie Mellon
Web traffic
• [Crovella Bestavros, SIGMETRICS’96]
ICAST, June'09 C. Faloutsos 10
School of Computer ScienceCarnegie Mellon
...
survivable,self-managing storage
infrastructure
...
a storage brick(0.5–5 TB)~1 PB
“self-*” = self-managing, self-tuning, self-healing, …
Self-* Storage (Ganger+)
ICAST, June'09 C. Faloutsos 11
School of Computer ScienceCarnegie Mellon
...
survivable,self-managing storage
infrastructure
...
a storage brick(0.5–5 TB)~1 PB
“self-*” = self-managing, self-tuning, self-healing, … Goal: 1 petabyte (PB) www.pdl.cmu.edu/SelfStar
Self-* Storage (Ganger+)
ICAST, June'09 C. Faloutsos 12
School of Computer ScienceCarnegie Mellon
Problem definition
• Given: one or more sequences x1 , x2 , … , xt , …; (y1, y2, … , yt, …)
• Find – patterns; clusters; outliers; forecasts;
ICAST, June'09 C. Faloutsos 13
School of Computer ScienceCarnegie Mellon
Problem
• Find patterns, in large datasets
time
# bytes
ICAST, June'09 C. Faloutsos 14
School of Computer ScienceCarnegie Mellon
Problem
• Find patterns, in large datasets
time
# bytes
Poisson indep., ident. distr
ICAST, June'09 C. Faloutsos 15
School of Computer ScienceCarnegie Mellon
Problem
• Find patterns, in large datasets
time
# bytes
Poisson indep., ident. distr
ICAST, June'09 C. Faloutsos 16
School of Computer ScienceCarnegie Mellon
Problem
• Find patterns, in large datasets
time
# bytes
Poisson indep., ident. distr
Q: Then, how to generatesuch bursty traffic?
ICAST, June'09 C. Faloutsos 17
School of Computer ScienceCarnegie Mellon
Solutions
• New tools: power laws, self-similarity and ‘fractals’ work, where traditional assumptions fail
• Let’s see the details:
ICAST, June'09 C. Faloutsos 18
School of Computer ScienceCarnegie Mellon
Overview
• Goals/ motivation: find patterns in large datasets:– (A) Sensor data– (B) network data
• Solutions: self-similarity and power laws
• Discussion
ICAST, June'09 C. Faloutsos 19
School of Computer ScienceCarnegie Mellon
What is a fractal?
= self-similar point set, e.g., Sierpinski triangle:
...zero area: (3/4)^inf
infinite length!
(4/3)^inf
Q: What is its dimensionality??
ICAST, June'09 C. Faloutsos 20
School of Computer ScienceCarnegie Mellon
What is a fractal?
= self-similar point set, e.g., Sierpinski triangle:
...zero area: (3/4)^inf
infinite length!
(4/3)^inf
Q: What is its dimensionality??A: log3 / log2 = 1.58 (!?!)
ICAST, June'09 C. Faloutsos 21
School of Computer ScienceCarnegie Mellon
Intrinsic (‘fractal’) dimension
• Q: fractal dimension of a line?
• Q: fd of a plane?
ICAST, June'09 C. Faloutsos 22
School of Computer ScienceCarnegie Mellon
Intrinsic (‘fractal’) dimension
• Q: fractal dimension of a line?
• A: nn ( <= r ) ~ r^1(‘power law’: y=x^a)
• Q: fd of a plane?• A: nn ( <= r ) ~ r^2fd== slope of (log(nn) vs..
log(r) )
ICAST, June'09 C. Faloutsos 23
School of Computer ScienceCarnegie Mellon
Sierpinsky triangle
log( r )
log(#pairs within <=r )
1.58
== ‘correlation integral’
= CDF of pairwise distances
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School of Computer ScienceCarnegie Mellon
Observations: Fractals <-> power laws
Closely related:
• fractals <=>
• self-similarity <=>
• scale-free <=>
• power laws ( y= xa ; F=K r-2)
• (vs y=e-ax or y=xa+b)log( r )
log(#pairs within <=r )
1.58
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School of Computer ScienceCarnegie Mellon
Outline
• Problems
• Self-similarity and power laws
• Solutions to posed problems
• Discussion
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School of Computer ScienceCarnegie Mellon
time
#bytes
Solution #1: traffic
• disk traces: self-similar: (also: [Leland+94])• How to generate such traffic?
ICAST, June'09 C. Faloutsos 27
School of Computer ScienceCarnegie Mellon
Solution #1: traffic
• disk traces (80-20 ‘law’) – ‘multifractals’
time
#bytes
20% 80%
ICAST, June'09 C. Faloutsos 28
School of Computer ScienceCarnegie Mellon
80-20 / multifractals20 80
ICAST, June'09 C. Faloutsos 29
School of Computer ScienceCarnegie Mellon
80-20 / multifractals20
• p ; (1-p) in general
• yes, there are dependencies
80
ICAST, June'09 C. Faloutsos 30
School of Computer ScienceCarnegie Mellon
More on 80/20: PQRS
• Part of ‘self-* storage’ project
time
cylinder#
ICAST, June'09 C. Faloutsos 31
School of Computer ScienceCarnegie Mellon
More on 80/20: PQRS
• Part of ‘self-* storage’ project
p q
r s
q
r s
ICAST, June'09 C. Faloutsos 32
School of Computer ScienceCarnegie Mellon
Overview
• Goals/ motivation: find patterns in large datasets:– (A) Sensor data
– (B) network data
• Solutions: self-similarity and power laws– sensor/traffic data
– network data
• Discussion
ICAST, June'09 C. Faloutsos 33
School of Computer ScienceCarnegie Mellon
Problem dfn
<source-ip, target-ip, timestamp, alert-type>
eg., <192.168.2.5; 128.2.220.159; 3am june 6; ICMP-redirect-host>
goal: find patterns / anomalies
ICAST, June'09 C. Faloutsos 34
School of Computer ScienceCarnegie Mellon
Power laws in intrusion data
rank
count
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School of Computer ScienceCarnegie Mellon
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School of Computer ScienceCarnegie Mellon
human-like
robot-like
robot-like (bursty)
Q: Can we visuallysummarize / classifyour sequences?
ICAST, June'09 C. Faloutsos 37
School of Computer ScienceCarnegie Mellon
Answer: yes!
two features:
• F1: how periodic (24h-cycle) is a sequence
• F2: how bursty it is
Q: how to measure burstiness?
A: Fractal dimension!
ICAST, June'09 C. Faloutsos 38
School of Computer ScienceCarnegie Mellon
Burstiness & f.d.
uniform: fd = 1
@same time-tick: fd = 0
ICAST, June'09 C. Faloutsos 39
School of Computer ScienceCarnegie Mellon
Burstiness & f.d.
uniform: fd = 1
@same time-tick: fd = 0
bursts within bursts within bursts:0<fd<1
ICAST, June'09 C. Faloutsos 40
School of Computer ScienceCarnegie Mellon
6. Proposed Methods: The FDP Plot
• Notice: clustering wrt alert types!
ICAST, June'09 C. Faloutsos 41
School of Computer ScienceCarnegie Mellon
human-like
robot-like
robot-like (bursty)
can we visuallysummarize / classifyour sequences?
ICAST, June'09 C. Faloutsos 42
School of Computer ScienceCarnegie Mellon
Exampleshuman-like behavior
ICAST, June'09 C. Faloutsos 43
School of Computer ScienceCarnegie Mellon
Exampleshuman-like behavior
ICAST, June'09 C. Faloutsos 44
School of Computer ScienceCarnegie Mellon
Exampleshuman-like behavior
ICAST, June'09 C. Faloutsos 45
School of Computer ScienceCarnegie Mellon
Exampleshuman-like behavior
ICAST, June'09 C. Faloutsos 46
School of Computer ScienceCarnegie Mellon
Outline
• problems
• Fractals
• Solutions
• Discussion – what else can they solve? – how frequent are fractals?
ICAST, June'09 C. Faloutsos 47
School of Computer ScienceCarnegie Mellon
What else can they solve?
• separability [KDD’02]• forecasting [CIKM’02]• dimensionality reduction [SBBD’00]• non-linear axis scaling [KDD’02]• disk trace modeling [PEVA’02]• selectivity of spatial/multimedia queries
[PODS’94, VLDB’95, ICDE’00]• ...
ICAST, June'09 C. Faloutsos 48
School of Computer ScienceCarnegie Mellon
Problem #3 - spatial d.m.
Galaxies (Sloan Digital Sky Survey w/ B. Nichol) - ‘spiral’ and ‘elliptical’
galaxies
- patterns? (not Gaussian; not uniform)
-attraction/repulsion?
- separability??
ICAST, June'09 C. Faloutsos 49
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Solution#3: spatial d.m.
log(r)
log(#pairs within <=r )
spi-spi
spi-ell
ell-ell
- 1.8 slope
- plateau!
- repulsion!
CORRELATION INTEGRAL!
ICAST, June'09 C. Faloutsos 50
School of Computer ScienceCarnegie Mellon
Solution#3: spatial d.m.
log(r)
log(#pairs within <=r )
spi-spi
spi-ell
ell-ell
- 1.8 slope
- plateau!
- repulsion!
[w/ Seeger, Traina, Traina, SIGMOD00]
ICAST, June'09 C. Faloutsos 51
School of Computer ScienceCarnegie Mellon
Solution#3: spatial d.m.
r1r2
r1
r2
Heuristic on choosing # of clusters
ICAST, June'09 C. Faloutsos 52
School of Computer ScienceCarnegie Mellon
Solution#3: spatial d.m.
log(r)
log(#pairs within <=r )
spi-spi
spi-ell
ell-ell
- 1.8 slope
- plateau!
- repulsion!
ICAST, June'09 C. Faloutsos 53
School of Computer ScienceCarnegie Mellon
Outline
• problems
• Fractals
• Solutions
• Discussion – what else can they solve? – how frequent are fractals?
ICAST, June'09 C. Faloutsos 54
School of Computer ScienceCarnegie Mellon
Fractals & power laws:
appear in numerous settings:
• medical
• geographical / geological
• social
• computer-system related
• <and many-many more! see [Mandelbrot]>
ICAST, June'09 C. Faloutsos 55
School of Computer ScienceCarnegie Mellon
Fractals: Brain scans
• brain-scans
octree levels
Log(#octants)
2.63 = fd
ICAST, June'09 C. Faloutsos 56
School of Computer ScienceCarnegie Mellon
More fractals
• periphery of malignant tumors: ~1.5
• benign: ~1.3
• [Burdet+]
ICAST, June'09 C. Faloutsos 57
School of Computer ScienceCarnegie Mellon
More fractals:
• cardiovascular system: 3 (!) lungs: ~2.9
ICAST, June'09 C. Faloutsos 58
School of Computer ScienceCarnegie Mellon
Fractals & power laws:
appear in numerous settings:
• medical
• geographical / geological
• social
• computer-system related
ICAST, June'09 C. Faloutsos 59
School of Computer ScienceCarnegie Mellon
More fractals:
• Coastlines: 1.2-1.58
1 1.1
1.3
ICAST, June'09 C. Faloutsos 60
School of Computer ScienceCarnegie Mellon
ICAST, June'09 C. Faloutsos 61
School of Computer ScienceCarnegie Mellon
More fractals:
• the fractal dimension for the Amazon river is 1.85 (Nile: 1.4)
[ems.gphys.unc.edu/nonlinear/fractals/examples.html]
ICAST, June'09 C. Faloutsos 62
School of Computer ScienceCarnegie Mellon
More fractals:
• the fractal dimension for the Amazon river is 1.85 (Nile: 1.4)
[ems.gphys.unc.edu/nonlinear/fractals/examples.html]
ICAST, June'09 C. Faloutsos 63
School of Computer ScienceCarnegie Mellon
Cross-roads of Montgomery county:
•any rules?
GIS points
ICAST, June'09 C. Faloutsos 64
School of Computer ScienceCarnegie Mellon
GIS
A: self-similarity:• intrinsic dim. = 1.51
log( r )
log(#pairs(within <= r))
1.51
ICAST, June'09 C. Faloutsos 65
School of Computer ScienceCarnegie Mellon
Examples:LB county
• Long Beach county of CA (road end-points)
1.7
log(r)
log(#pairs)
ICAST, June'09 C. Faloutsos 66
School of Computer ScienceCarnegie Mellon
More power laws: areas – Korcak’s law
Scandinavian lakes
Any pattern?
ICAST, June'09 C. Faloutsos 67
School of Computer ScienceCarnegie Mellon
More power laws: areas – Korcak’s law
Scandinavian lakes area vs complementary cumulative count (log-log axes)
log(count( >= area))
log(area)
ICAST, June'09 C. Faloutsos 68
School of Computer ScienceCarnegie Mellon
More power laws: Korcak
Japan islands;
area vs cumulative count (log-log axes) log(area)
log(count( >= area))
ICAST, June'09 C. Faloutsos 69
School of Computer ScienceCarnegie Mellon
More power laws
• Energy of earthquakes (Gutenberg-Richter law) [simscience.org]
log(count)
Magnitude = log(energy)day
Energy released
ICAST, June'09 C. Faloutsos 70
School of Computer ScienceCarnegie Mellon
Fractals & power laws:
appear in numerous settings:
• medical
• geographical / geological
• social
• computer-system related
ICAST, June'09 C. Faloutsos 71
School of Computer ScienceCarnegie Mellon
A famous power law: Zipf’s law
• Bible - rank vs. frequency (log-log)
log(rank)
log(freq)
“a”
“the”
“Rank/frequency plot”
ICAST, June'09 C. Faloutsos 72
School of Computer ScienceCarnegie Mellon
TELCO data
# of service units
count ofcustomers
‘best customer’
ICAST, June'09 C. Faloutsos 73
School of Computer ScienceCarnegie Mellon
SALES data – store#96
# units sold
count of products
“aspirin”
ICAST, June'09 C. Faloutsos 74
School of Computer ScienceCarnegie Mellon
Olympic medals (Sidney’00, Athens’04):
log( rank)
log(#medals)
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2
athens
sidney
ICAST, June'09 C. Faloutsos 75
School of Computer ScienceCarnegie Mellon
Olympic medals (Sidney’00, Athens’04):
log( rank)
log(#medals)
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2
athens
sidney
ICAST, June'09 C. Faloutsos 76
School of Computer ScienceCarnegie Mellon
Even more power laws:
• Income distribution (Pareto’s law)• size of firms
• publication counts (Lotka’s law)
ICAST, June'09 C. Faloutsos 77
School of Computer ScienceCarnegie Mellon
Even more power laws:
library science (Lotka’s law of publication count); and citation counts: (citeseer.nj.nec.com 6/2001)
log(#citations)
log(count)
Ullman
ICAST, June'09 C. Faloutsos 78
School of Computer ScienceCarnegie Mellon
Even more power laws:
• web hit counts [w/ A. Montgomery]
Web Site Traffic
log(freq)
log(count)
Zipf“yahoo.com”
ICAST, June'09 C. Faloutsos 79
School of Computer ScienceCarnegie Mellon
Autonomous systems• Power law in the degree distribution [SIGCOMM99]
internet domains
log(rank)
log(degree)
-0.82
att.com
ibm.com
ICAST, June'09 C. Faloutsos 80
School of Computer ScienceCarnegie Mellon
The Peer-to-Peer Topology
• Number of immediate peers (= degree), follows a power-law
[Jovanovic+]
degree
count
ICAST, June'09 C. Faloutsos 81
School of Computer ScienceCarnegie Mellon
epinions.com
• who-trusts-whom [Richardson + Domingos, KDD 2001]
(out) degree
count
ICAST, June'09 C. Faloutsos 82
School of Computer ScienceCarnegie Mellon
Fractals & power laws:
appear in numerous settings:
• medical
• geographical / geological
• social
• computer-system related
ICAST, June'09 C. Faloutsos 83
School of Computer ScienceCarnegie Mellon
Power laws, cont’d
• In- and out-degree distribution of web sites [Barabasi], [IBM-CLEVER]
log indegree
- log(freq)
from [Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins ]
ICAST, June'09 C. Faloutsos 84
School of Computer ScienceCarnegie Mellon
Power laws, cont’d
• In- and out-degree distribution of web sites [Barabasi], [IBM-CLEVER]
log indegree
log(freq)
from [Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins ]
ICAST, June'09 C. Faloutsos 85
School of Computer ScienceCarnegie Mellon
Power laws, cont’d
• In- and out-degree distribution of web sites [Barabasi], [IBM-CLEVER]
log indegree
log(freq)
Q: ‘how can we usethese power laws?’
ICAST, June'09 C. Faloutsos 86
School of Computer ScienceCarnegie Mellon
“Foiled by power law”
• [Broder+, WWW’00]
(log) in-degree
(log) count
ICAST, June'09 C. Faloutsos 87
School of Computer ScienceCarnegie Mellon
“Foiled by power law”
• [Broder+, WWW’00]
“The anomalous bump at 120on the x-axis is due a large clique formed by a single spammer”
(log) in-degree
(log) count
ICAST, June'09 C. Faloutsos 88
School of Computer ScienceCarnegie Mellon
Power laws, cont’d
• In- and out-degree distribution of web sites [Barabasi], [IBM-CLEVER]
• length of file transfers [Crovella+Bestavros ‘96]
• duration of UNIX jobs
ICAST, June'09 C. Faloutsos 89
School of Computer ScienceCarnegie Mellon
Conclusions
• Fascinating problems in Data Mining: find patterns in streams & network data
ICAST, June'09 C. Faloutsos 90
School of Computer ScienceCarnegie Mellon
Conclusions - cont’d
New tools for Data Mining: self-similarity & power laws: appear in many cases
Bad news:
lead to skewed distributions
(no Gaussian, Poisson,
uniformity, independence,
mean, variance)
Good news:• ‘correlation integral’
for separability• rank/frequency plots• 80-20 (multifractals)• (Hurst exponent, • strange attractors,• renormalization theory, • ++)
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School of Computer ScienceCarnegie Mellon
Resources
• Manfred Schroeder “Chaos, Fractals and Power Laws”, 1991
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References
• [vldb95] Alberto Belussi and Christos Faloutsos, Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension Proc. of VLDB, p. 299-310, 1995
• [Broder+’00] Andrei Broder, Ravi Kumar , Farzin Maghoul1, Prabhakar Raghavan , Sridhar Rajagopalan , Raymie Stata, Andrew Tomkins , Janet Wiener, Graph structure in the web , WWW’00
• M. Crovella and A. Bestavros, Self similarity in World wide web traffic: Evidence and possible causes , SIGMETRICS ’96.
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References
• J. Considine, F. Li, G. Kollios and J. Byers, Approximate Aggregation Techniques for Sensor Databases (ICDE’04, best paper award).
• [pods94] Christos Faloutsos and Ibrahim Kamel, Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension, PODS, Minneapolis, MN, May 24-26, 1994, pp. 4-13
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References
• [vldb96] Christos Faloutsos, Yossi Matias and Avi Silberschatz, Modeling Skewed Distributions Using Multifractals and the `80-20 Law’ Conf. on Very Large Data Bases (VLDB), Bombay, India, Sept. 1996.
• [sigmod2000] Christos Faloutsos, Bernhard Seeger, Agma J. M. Traina and Caetano Traina Jr., Spatial Join Selectivity Using Power Laws, SIGMOD 2000
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References
• [vldb96] Christos Faloutsos and Volker Gaede Analysis of the Z-Ordering Method Using the Hausdorff Fractal Dimension VLD, Bombay, India, Sept. 1996
• [sigcomm99] Michalis Faloutsos, Petros Faloutsos and Christos Faloutsos, What does the Internet look like? Empirical Laws of the Internet Topology, SIGCOMM 1999
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References
• [Leskovec 05] Jure Leskovec, Jon M. Kleinberg, Christos Faloutsos: Graphs over time: densification laws, shrinking diameters and possible explanations. KDD 2005: 177-187
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References
• [ieeeTN94] W. E. Leland, M.S. Taqqu, W. Willinger, D.V. Wilson, On the Self-Similar Nature of Ethernet Traffic, IEEE Transactions on Networking, 2, 1, pp 1-15, Feb. 1994.
• [brite] Alberto Medina, Anukool Lakhina, Ibrahim Matta, and John Byers. BRITE: An Approach to Universal Topology Generation. MASCOTS '01
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References
• [icde99] Guido Proietti and Christos Faloutsos, I/O complexity for range queries on region data stored using an R-tree (ICDE’99)
• Stan Sclaroff, Leonid Taycher and Marco La Cascia , "ImageRover: A content-based image browser for the world wide web" Proc. IEEE Workshop on Content-based Access of Image and Video Libraries, pp 2-9, 1997.
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References
• [kdd2001] Agma J. M. Traina, Caetano Traina Jr., Spiros Papadimitriou and Christos Faloutsos: Tri-plots: Scalable Tools for Multidimensional Data Mining, KDD 2001, San Francisco, CA.
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School of Computer ScienceCarnegie Mellon
Thank you!
Contact info:christos <at> cs.cmu.edu
www. cs.cmu.edu /~christos
(w/ papers, datasets, code for fractal dimension estimation, etc)