This document discusses visual data mining using HeatMiner software. It shows how increasing amounts of sample data (from 10 to 100,000 samples) shapes visually into patterns that reveal relationships between a value of interest and other attributes. With only 10,000 samples, the data forms three distinct "donuts", and adding more samples simply reduces noise without changing the overall patterns or conclusions that can be drawn from the data.

1. Visual Data Mining
HeatMiner® is a registered trademark of Agience Oy Ltd
by Pauli Misikangas, the CEO of Cloud’N’Sci Ltd

2. Mysterious Data
The Value Possibly Related
of Interest Attributes
How does the Value of Interest depend on X,Y,Z ?
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3. 10 Samples
Colors indicate the
Value of Interest
(xyz+color = 4D visualization)
Samples separated,
Attributes no visual patterns
X,Y and Z → more data needed
define a
3D space
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4. 100 Samples
Adjacent samples
merge and form a
’heatmap’ in which
colors indicate the
average value
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5. 1000 Samples
The secrets
of the data
are shaping
visually...
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6. 10000 Samples
Now it’s easy to see
that data points
form three ’donuts’!
HeatMiner® is a registered trademark of Agience Oy Ltd
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7. 100000 Samples
Further samples
reduce ’bumpiness’
of the 4D heatmap
The visual illusion
and conclusions are
still the same – no
more data needed
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8. Visual Data Mining
RIGHT
!?! CONCLUSIONS
& ACTIONS
AUTOMATIC
Raw input DATA REFINING Visual illusion
data ALGORITHMS of the data
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9. Questions? Comments? Ideas? Use Cases?
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