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Use case type analysis of the process.
A.4: Pair wise semantics will drive a clustering process to produce a new semantically endowed topology on the circle.
A.4.1: Features of the new space are identified by the nearness of representational points on the circle.
A.4.2: Prueitt feature extraction is seen to be equivalent to Kohonen feature extraction
A.4.3: Prueitt feature extraction produces a specific distribution of points on the circle.
A.4.3.1: The Prueitt distribution is defined as a limiting distribution which has semantically relevant clusters
A.4.3.2: As in associative neural networks, and other feature extraction algorithms, the Prueitt distribution is driven by a randomization of selection (of the order of pairing evaluation), and thus is not unique to that data set.
A.4.3.3: Iterated feature extraction produces a quasi- complete enumeration of features, encoded within a topology.
A.4.3.4: The limiting distribution has a endowed semantics.
A.4.3.5: The clustering process can be mediated by human perceptual acuity
A.4.3.5.1: Phrase to Phrase association by human introspection is coded in a relational matrix.
A.4.3.5.2: Latent Semantic Indexing can be done for automatically constructing co-occurrence, correlation and associative matrices.