Table des matières

# Fuzzy Concept Extractor

While “Fuzzy Concept Extractor” might seem like a specialization of “Concept Extractor”, it is a generalization compared to concept extractors for binary relations and multivalued crisp relations (“one value among”–think radio buttons).

If a fuzzy trait can have several modalities to varying degrees, with the sum of degrees (or affinities)

 Fuzzy (or composite) Trait1) Multiple choice Fuzzy (another) Binary Binary Red Green (Crisp) (explicit) (usual compact Y/N) 1 0 X daytime 0 1 _ night time 1 60% 40% evening? dawn?

So (for quantified descriptors)

• a rated pattern is an ordered set of numeric values associated with descriptors
• a normed histogram (or affinities or belief weights?) is a pattern whose descriptors must add to 100%
• a multi-valued (scaled) attribute is a histogram which concentrates the 100% on a single modality
• a binary-valued attribute is a multi-valued attribute with only two modalities
• mostly abbreviated as “on/off”

# Classes and Pseudocode

• FuzzyConceptExtractor
• input : file of context to analyse, or
• FuzzyContext
• Trait*Modality framework
• Traits
• Modalities
• Norm Constraint (if sum of affinities must equal a set value: 100 for percents, 1000 for per mil, etc.)
• Trait-Modalities segmentation : for decoding (segmenting, parsing) the serialized affinities and applying the norm constraints.
• Specimens : TreeSet
• Specimen.name
• Specimen.affinities: serialized affinities for each trait.modality, in order
• Constructors
• TreeSet (Specimins is not a specialized class); Specimen have a lexicographic compareTo().
• FuzzyConcepts <TreeSet> : output
• FuzzyConcept
• Constructors
• FuzzyConcept(FuzzyConcept prior, FuzzyContext context, Specimen candidate) :
• FuzzyIntent of prior relaxed to admit candidate
• FuzzyExtent closed with respect to new FuzzyIntent for context.specimens.
• FuzzyConcept(FuzzyContext context, Specimen candidate)
• FuzzyIntent(Specimen candidate)
• FuzzyExtent(FuzzyIntent intent, FuzzyContext context, Specimen currentSpecimen)
• FuzzyIntent
• minAffinities : integer vector
• maxAffinities : integer vector
• Constructors
• FuzzyIntent(Specimen founder) : create an intent from a specimen (min=max=specimen.affinities)
• FuzzyIntent(FuzzyIntent prior, Specimen candidate) : create an intent from prior, relaxed to admit candidate.
• FuzzyExtent
• TreeSet <Specimen>
• Constructors
• FuzzyExtent(FuzzyIntent intent, FuzzyContext context) : list of all specimens in context which intent.admits().
• FuzzyExtent(FuzzyIntent intent, FuzzyContext context, Specimen currentSpecimen) :
• list of all specimens in context which intent.admits() if none are predecessors of currentSpecimen,
• null otherwise.
• FuzzyExtent(FuzzyIntent intent, FuzzyContext context, Specimen currentSpecimen, FuzzyExtent prior) :
• prior plus list of all other specimens in context which intent.admits() if none are predecessors of currentSpecimen,
• null otherwise.

## Algorithm

• FuzzyConceptExtractor constructs FuzzyContext with normed affinities, specimens sorted by normed affinities in lexicographic order.
• For each specimen in FuzzyContext, FCE generates its FuzzyConcept
• If new (not redundant)
• For each successor specimen, derive relaxed FuzzyConcept – FuzzyConcept(FuzzyConcept prior, FuzzyContext context, Specimen candidate) – to include the candidate. If new, continue recursively. This will have to be done either via a FCE method or passing the FCE concept tree as an argument to be able to add each new concept to the tree.
1) conceivably, in a case like this example, luminosity could be unconstrained, but the RGB values could be constrained to produce that luminosity. The RGB values represent a pattern but not a histogram normed to 100%

java/fuzzy_concept_extractor.txt · Dernière modification: 2010/03/31 13:18 par suitable

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