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人工智能英文版课件:Design of information granules.pptx
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2022-06-20
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人工智能英文版课件:Design of information granules.pptx
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Design of information granules
The principle of justiable granularity
Construction of information granules via fuzzy clustering
Knowledge-based clustering
Successive renements of information granules
Outline
The principle of justiable granularity
Given
collection of a one-dimensional (scalar) numeric data, D = {x
1
, x
2
, …, x
N
}.
-
form a meaningful (legitimate) information granule based which adheres to
two intuitively compelling requirements:
(i) experimental evidence (legitimacy)
(ii) sound semantic (meaning)
The principle of justiable granularity –
experimental evidence
(i) experimental evidence (legitimacy):
The numeric evidence accumulated within the bounds of W
has to be as high as possible.
The principle of justiable granularity –
semantic soundness
(ii) sound semantic (meaning)
information granule should be as specic as possible.
This request implies that the resulting information granule comes with
a well-dened semantics (meaning). We would like to have W highly
detailed, which makes the information granule semantically meaningful
(sound).
This implies that the smaller (more compact) the information granule
(lower information granule) is, the better.
This point of view is in agreement with our general perception of
knowledge being articulated through constraints (information granules)
“x is in [1,3]” is more specic (semantically sound, more supportive of
any further action, etc.) than “x is in [0, 10]”.
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