Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily ...

Cognitive approaches to knowledge representation improve man-machine communication, as they are close to human reasoning. Conceptual spaces have been proposed as one such knowledge formalization method. Our research investigates the theory of conceptual spaces as a methodology for implementing semantic reference systems. Conceptual spaces are spanned by quality dimensions. Concepts are represen...

Conceptual clustering is an important way to summarize data in an understandable manner. However, the recency of the conceptual clustering paradigm has allowed little exploration of conceptual clustering as a means of improving performance. This paper presents COBWEB, a conceptual clustering system that organizes data to maximize inference abilities. It does this by capturing attribute inter-co...

The article deals with the urgent task of improving universal decimal classification (UDC) in connection its numerous shortcomings. examples given show that well-known rules for constructing classifications are violated UDC and system principles models not applied. These circumstances turn indexing into a complex extremely time-consuming creative, heuristic process, result which many cases, pri...

In this paper, we present an original approach for text summarization using conceptual data classification. We show how a given text can be summarized without losing meaningful knowledge and without using any semantic or grammatical concepts. In fact, concept date classification is used to extract the most interacting sentences from the main text and ignoring the other meaningless sentences in ...