One of the major problems of data mining systems is the identification of classes, categories, and concepts. We introduce a new framework for categorization which is based on the concept of "pattern conception" (a term that may be contrasted to "pattern recognition", "pattern matching", "pattern perception", etc.). There are important distinctions between pattern conception and the mainstream pattern recognition models; furthermore, these distinctions lead us to new categorization information-processing architectures. The first major distinction tells us that there is more than one correct conception for each individual pattern. Each pattern may have numerous segmentations and descriptions which are fundamentally distinct but equally correct in a deep sense. Another striking distinction of pattern conception is the capability to "see as", in which context will guide the interpretation of data such as that one object may be seen as if it were another type of object, or as if it were occupying the position or role of other objects. A final and related distinction is that there should be a 'relativity theory' view of concepts and categories, in which concepts are both defined by their relations to other concepts and activated from the spread of activation of other concepts. In this work, we analyze how these distinctions appear under three distinct application domains: (Ⅰ) the notorious case of Bongard problems; (ⅱ) letter-string analogies; and (ⅲ) the game of chess (viewed as a pattern analysis problem). It may be concluded that data mining methods must be able to handle these distinctions if they are to be effective at pattern conception, and, thus, to a wide class of information categorization problems.
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