Data Compression by
Quantization
Edward J. Wegman
Center for Computational Statistics
George Mason University
Outline
Acknowledgements
Complexity
Sampling Versus Binning
Some Quantization Theory
Recommendations for Quantization
Acknowledgements
This is joint work with Nkem

Amin (Martin) Khumbah
This work was funded by the Army Research Office
Complexity
Descriptor
Data Set Size in Bytes
Storage Mode
Tiny
10
2
Piece of Paper
Small
10
4
A Few Pieces of
Paper
Medium
10
6
A Floppy Disk
Large
10
8
Hard Disk
Huge
10
10
Multiple Hard Disks
e.g. RAID Storage
Massive
10
12
Robotic Magnetic
Tape
Storage Silos
Super Massive
10
15
Distributed Archives
The Huber/Wegman Taxonomy of Data Set Sizes
Complexity
O(r)
Plot a scatterplot
O(n)
Calculate means, variances, kernel density
estimates
O(n log(n))
Calculate fast Fourier transforms
O(nc)
Calculate singular value decomposition of
an
rc matrix; solve a multiple linear
regression
O(n
2
)
Solve most clustering algorithms.
O(a
n
)
Detect Multivariate Outliers
Algorithmic Complexity
Complexity
Table 7: Computational Feasibility on a Teraflop Grand Challenge Computer
1000 gigaflop performance assumed
n
n
1/2
n
n log(n)
n
3/2
n
2
tiny
10
11
seconds
10
10
seconds
2x10
10
seconds
10
9
seconds
10
8
seconds
small
10
10
seconds
10
8
seconds
4x10
8
seconds
10
6
seconds
10
4
seconds
medium
10
9
seconds
10
6
seconds
6x10
6
seconds
.001
seconds
1
second
large
10
8
seconds
10
4
seconds
8x10
4
seconds
1
second
2.8
hours
huge
10
7
seconds
.01
seconds
.1
seconds
16.7
minutes
3.2
years
Motivation
Massive data sets can make many
algorithms computationally infeasible, e.g.
O(n
2
) and higher
Must reduce effective number of cases
Reduce computational complexity
Reduce data transfer requirements
Enhance visualization capabilities
Data Sampling
Database Sampling
Exhaustive search may not be practically feasible
because of their size
The KDD systems must be able to assist in the
selection of appropriate parts if the databases to be
examined
For sampling to work, the data must satisfy certain
conditions (not ordered, no systematic biases)
Sampling can be very expensive operation especially
when the sample is taken from data stored in a
DBMS. Sampling 5% of the database can be more
expensive that a sequential full scan of the data.
Data Compression
Squishing, Squashing, Thinning, Binning
Squishing = # cases reduced
Sampling = Thinning
Quantization = Binning
Squashing = # dimensions (variables) reduced
Depending on goal, one of sampling or quantization
may be preferable
Data Quantization
Thinning vs Binning
People’s first thoughts about Massive Data
usually is statistical subsampling
Quantization is engineering’s success
story
Binning is statistician’s quantization
Data Quantization
Images are quantized in 8 to 24 bits, i.e. 256 to
16 million levels.
Signals (audio on CDs) are quantized in 16 bits,
i.e. 65,536 levels
Ask a statistician how many bins to use, likely
response is a few hundred, ask a CS data miner,
likely response is 3
For a terabyte data set, 10
6
bins
Data Quantization
Binning, but at microresolution
Conventions
d = dimension
k = # of bins
n = sample size
Typically k << n
Data Quantization
Choose E[WQ = y
j
] = mean of
observations in j
th
bin = y
j
In other words, E[WQ] = Q
The quantizer is self

consistent
Data Quantization
E[W] = E[Q]
If
is a linear unbiased estimator, then so is E[
Q]
If h is a convex function, then E[h(Q)]
E[h(W)].
In particular, E[Q
2
]
E[W
2
] and var (Q)
var (W).
E[Q(Q

W)] = 0
cov (W

Q) = cov (W)

cov (Q)
E[W

P]
2
E[W

Q]
2
where P is any other quantizer.
Data Quantization
Distortion due to Quantization
Distortion is the error due to quantization.
In simple terms, E[W

Q]
2
.
Distortion is minimized when the
quantization regions, S
j
, are most like a
(hyper

) sphere.
Geometry

based Quantization
Need space

filling tessellations
Need congruent tiles
Need as spherical as possible
Geometry

based Quantization
In one dimension
Only polytope is a straight line segment (also
bounded by a one

dimensional sphere).
In two dimensions
Only polytopes are equilateral triangles, squares and
hexagons
Geometry

based Quantization
In 3 dimensions
Tetrahedrons (3

simplex), cube, hexagonal prism,
rhombic dodecahedron, truncated octahedron.
In 4 dimensions
4 simplex, hypercube, 24 cell
Truncated octahedron
tessellation
Geometry

based Quantization
Tetrahedron
*
.1040042…
Cube
*
.0833333…
Octahedron
.0825482…
Hexagonal Prism
*
.0812227…
Rhombic Dodecahedron
*
.0787451…
Truncated Octahedron
*
.0785433…
Dodecahedron
.0781285…
Icosahedron
.0778185…
Sphere
.0769670
Dimensionless Second Moment for 3

D Polytopes
Geometry

based Quantization
Tetrahedron
Cube
Octahedron
Icosahedron
Dodecahedron
Truncated
Octahedron
Geometry

based Quantization
Rhombic Dodecahedron
http://www.jcrystal.com/steffenweber/POLYHEDRA/p_07.html
Geometry

based Quantization
Hexagonal Prism
24 Cell with Cuboctahedron Envelope
Geometry

based Quantization
Using 10
6
bins is computationally and visually feasible.
Fast binning, for data in the range [a,b], and for k bins
j = fixed[k*(x
i

a)/(b

a)]
gives the index of the bin for x
i
in one dimension.
Computational complexity is 4n+1=O(n).
Memory requirements drop to 3k

location of bin + #
items in bin + representor of bin, I.e. storage
complexity is 3k.
Geometry

based Quantization
In two dimensions
Each hexagon is indexed by 3 parameters.
Computational complexity is 3 times 1

D complexity,
I.e. 12n+3=O(n).
Complexity for squares is 2 times 1

D complexity.
Ratio is 3/2.
Storage complexity is still 3k.
Geometry

based Quantization
In 3 dimensions
For truncated octahedron, there are 3 pairs of square
sides and 4 pairs of hexagonal sides.
Computational complexity is 28n+7 = O(n).
Computational complexity for a cube is 12n+3.
Ratio is 7/3.
Storage complexity is still 3k.
Quantization Strategies
Optimally for purposes of minimizing distortion,
use roundest polytope in d

dimensions.
Complexity is always O(n).
Storage complexity is 3k.
# tiles grows exponentially with dimension, so

called
curse of dimensionality.
Higher dimensional geometry is poorly known.
Computational complexity grows faster than
hypercube.
Quantization Strategies
For purposes of simplicity, always use hypercube or d

dimensional simplices
Computational complexity is always O(n).
Methods for data adaptive tiling are available
Storage complexity is 3k.
# tiles grows exponentially with dimension.
Both polytopes depart spherical shape rapidly as d increases.
Hypercube approach is known as datacube in computer science
literature and is closely related to multivariate histograms in
statistical literature.
Quantization Strategies
Conclusions on Geometric Quantization
Geometric approach good to 4 or 5 dimensions.
Adaptive tilings may improve rate at which # tiles
grows, but probably destroy spherical structure.
Good for large n, but weaker for large d.
Quantization Strategies
Alternate Strategy
Form bins via clustering
Known in the electrical engineering literature as vector
quantization.
Distance based clustering is O(n
2
) which implies poor
performance for large n.
Not terribly dependent on dimension, d.
Clusters may be very out of round, not even convex.
Conclusion
Cluster approach may work for large d, but fails for large n.
Not particularly applicable to “massive” data mining.
Quantization Strategies
Third strategy
Density

based clustering
Density estimation with kernel estimators is O(n).
Uses modes m
to form clusters
Put x
i
in cluster
if it is closest to mode m
.
This procedure is distance based, but with complexity O(kn)
not O(n
2
).
Normal mixture densities may be an alternative approach.
Roundness may be a problem.
But quantization based on density

based clustering
offers promise for both large d and large n.
Data Quantization
Binning does not lose fine structure in tails as sampling
might.
Roundoff analysis applies.
With scale of binning, discretization not likely to be
much less accurate than accuracy of recorded data.
Discretization

finite number of bins implies discrete
variables more compatible with categorical data.
Data Quantization
Analysis on a finite subset of the integers
has theoretical advantages
Analysis is less delicate
different forms of convergence are equivalent
Analysis is often more natural since data is
already quantized or categorical
Graphical analysis of numerical data is not
much changed since 10
6
pixels is at limit of
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