Download Visualization Techniques in Data Mining

Tecniche di Apprendimento Automatico per
Applicazioni di Data Mining
Visualization Techniques
in Data Mining
Prof. Pier Luca Lanzi
Laurea in Ingegneria Informatica
Politecnico di Milano
Polo di Milano Leonardo
Outline
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Goals of visualization
Advantages
Methodologies
Techniques
User interaction
Problems
© Pier Luca Lanzi
Goals of Data Visualization
• Today there is the need to manage a huge
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amount of data, and computer systems help
us in this task
Visual Data Mining help to deal with this
flood of information, integrating the human
in the data analysis process
Visual Data Mining allows the user to gain
insight into the data, drawing conclusions
and directly interacting with the data
© Pier Luca Lanzi
Advantages of visualization
techniques
The main advantages of the application of Visual
data mining techniques are:
• Visual data exploration can easily deal with very large,
highly non homogeneous and noisy amount of data
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Visual data exploration requires no understanding of
complex mathematical or statistical algorithms
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Visualization techniques provide a qualitative overview
useful for further quantitative analysis
© Pier Luca Lanzi
Approach methodologies
Presentation:
• starting point: facts to be presented are fixed a priori
• result: high-quality visualization of the data presenting the facts
Confirmative Analysis:
• starting point: hypotheses about the data
• result: visualization of the data allowing confirmation or rejection of
the hypotheses
Explorative Analysis:
• starting point: data without hypotheses
• result: visualization of the data, which can provide hypotheses
about data distribution
© Pier Luca Lanzi
Visualization techniques
• Geometric techniques: scatterplots matrices, Hyperslice,
parallel coordinates
• Pixel-oriented techniques: simple line-by-line, spiral and
circle segments
• Hierarchical techniques: Treemap, cone trees
• Graph-based techniques: 2D and 3D graph
• Distortion techniques: hyperbolic tree, fisheye view,
perspective wall
• User interaction: brushing, linking, dynamic projections and
rotations, dynamic queries
© Pier Luca Lanzi
Geometric techniques
Basic idea:
• Visualization of geometric transformations and
projections of the data
Methods:
• Scatterplot matrices
• Hyperslice
• Parallel coordinates
© Pier Luca Lanzi
Scatterplot matrices
• A scatterplot matrix
is composed of
scatter plots of all
possible pairs of
variables in a dataset
• Assuming a
N-dimension dataset,
there are (N2-N)/2
pairs of two
dimension plots
© Pier Luca Lanzi
Hyperslice
• HyperSlice is an
extension of the
scatterplot matrix
• They represent a
multi-dimensional
function as a
matrix of
orthogonal
two-dimensional
slices
© Pier Luca Lanzi
Parallel Coordinates
• The axes are defined as parallel
vertical lines separated
• A point in Cartesian coordinates
correspond to a polyline in
parallel coordinates
• Able to visualize data that may be
occluded in Cartesian coordinates
© Pier Luca Lanzi
Pixel-oriented techniques
Basic idea:
• The basic idea of pixel-oriented techniques is to map each
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data value to a colored pixel
Each attribute value is represented by a pixel with a color
tone proportional to a relevance factor in a separate
window
Methods:
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Simple Arrangement Line-by-Line
Spiral and Circle Segments Techniques
© Pier Luca Lanzi
Pixel-oriented techniques
• Simple arrangement line-by-line
© Pier Luca Lanzi
Pixel-oriented techniques
• Spiral
• Circle segments
© Pier Luca Lanzi
Hierarchical techniques
Basic idea:
Visualization of the data using a hierarchical
partitioning into two- or three-dimensional
subspaces
Methods:
• Treemap
• Cone trees
© Pier Luca Lanzi
Treemap
• Visualization of hierarchical collections of quantitative data as files
on a hard drive, financial analysis, bioinformatics, etc..
• Divide a limited screen space display area into a sequence of
rectangles whose areas correspond to an attribute of data set
http://www.smartmoney.com/marketmap/
© Pier Luca Lanzi
Cone trees
3-dimensional extension of the more familiar
2-D hierarchical tree structures, to a more
intuitive navigation and display of information
© Pier Luca Lanzi
Graph-based visualization
• Graphs (edges + nodes) with labels and
attributes
• Used where emphasis is on data relationship
(databases, telecom)
• Coordinates not always meaningful
• Useful for discovering patterns
© Pier Luca Lanzi
Graph-based visualization
• Color and thickness code values
• Asymmetric relations:
© Pier Luca Lanzi
Graph-based visualization
• E-mail (SeeNet)
© Pier Luca Lanzi
Graph-based visualization
• 3D graphs:
– more room for objects
– different points of view
• Example (hypertexts – Narcissus):
© Pier Luca Lanzi
Focus vs. context
• Too much data in too small screens
• Solutions:
– dual views (detailed + global)
– distorted view (e.g. fisheye view)
© Pier Luca Lanzi
Distortion
• Hyperbolic tree
• Fisheye view
• Perspective wall
© Pier Luca Lanzi
User interaction
• Brushing: selecting points or regions
• Linking: more views work together
© Pier Luca Lanzi
User interaction
• Dynamic projections and rotations
– Interactively and continuously moving through
subspaces
• Dynamic queries
– Visual interface (button and sliders)
– Incremental behavior (undo)
© Pier Luca Lanzi
Problems
• Missing attributes
– Ignore
– Fill blanks with:
• a predefined constant
• a value extracted according to the inferred
distribution
– Assess the effect of interpolated values
© Pier Luca Lanzi
Problems
• Large data sets
– Typical screens have one million pixels
– Subsampling
– Voxel/pixel bins
– Jittering
• Large number of attributes
– Principal component analysis
– Factor analysis
– Etc.
© Pier Luca Lanzi
Conclusions
• Human and computer skills can be integrated
with visual data mining
• Visualization may be useful for:
– understanding what is happening
– searching novel patterns
• User interaction is paramount in these
© Pier Luca Lanzi
References (I)
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D. A. Keim. “Visual Techniques for Exploring Databases”. Int.
Conference on Knowledge Discovery in Databases, 1997.
D. A. Keim. “Information visualization and visual data mining”. IEEE
Trans. on Visualization and Computer Graphics, jan 2002, vol. 8, no. 1,
pp. 1-8
J. Van Wijk, R. Van Liere. “HyperSlice - Visualization of scalar
functions of many variables”. IEEE Visualization, 1993, pp.119-125.
P. C. Wong, A. H. Crabb, R. D. Bergeron. “Dual multiresolution
HyperSlice for multivariate data visualization”. InfoVis 1996
D. A. Keim. “Pixel-oriented Database Visualizations”. SIGMOD
RECORD, Special Issue on Information Visualization, 1996.
M. Ankerst, D. A. Keim, H.-P. Kriegel. “Circle Segments: A Technique
for Visually Exploring Large Multidimensional Data Sets”. Visualization
'96, 1996.
B. B. Bederson, B. Shneiderman, M. Wattenberg. “Ordered and
Quantum Treemaps: Making Effective Use of 2D Space to Display
Hierarchies”. ACM Transactions on Graphics, 2002, pp. 833-854.
© Pier Luca Lanzi
References (II)
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R. A. Becker, S. G. Eick, A. R. Wilks. “Visualizing Network Data”.
IEEE Trans. on Visualization and Computer Graphics, mar 1995, vol. 1,
no. 1, pp. 16-28
R. J. Hendley, N. S. Drew, A. M. Wood, R. Beale. “Narcissus:
visualising information”. InfoVis 1995, p. 90
T. A. Keahey, E. L. Robertson (1996). “Techniques for non-linear
magnification transformations”. InfoVis 1996
J. Lamping, R. Rao, P. Pirolli. “A focus+context technique based on
hyperbolic geometry for visualizing large hierarchies”. CHI '95, pp.
401-408
J. D. Mackinlay, G. G. Robertson, S. K. Card. “The perspective wall:
detail and context smoothly integrated”. CHI '91, pp. 173-176
© Pier Luca Lanzi