Learning driven by the concepts structure
Initially, concepts are described in terms of values of attributes. These descriptions are in a probabilistic DNF form. Assuming a growing language, concepts already taught can be used in describing new concepts. The order of teaching the concepts is the key to producing their optimal descriptions. By "optimal" we mean the minimum number of occurences of constants in descriptions. The problem of finding the minimal description for each concept is NP-complete, hence our proposed algorithm has to be heuristic. Our strategy is based on clustering terms in concept descriptions in order to replace them by shorter higher level terms. Concepts are taught with respect to the increasing order of disjuncts in their descriptions. The outcome of the algorithm are optimized concepts descriptions in terms of a growing language, and a concept hierarchy that can be used for further learning and as a tool for generalization/specialization/similarity based reasoning.
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