LIST OF SYMBOLS L A ( complete ) lattice

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL¡framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, \missing" and \don't care" data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely \3⁄4 ¡ FLN" for competitive clustering, and \FLN with tightest ̄ts (FLNtf)" for supervised clustering. Learning by the 3⁄4 ¡ FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf , is incremental, data order independent, polynomial O(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The 3⁄4 ¡ FLN is presented here as fuzzyART's extension in the FL¡framework such that 3⁄4 ¡FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with \missing" and/or \don't care" attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL¡framework in computing is also delineated.