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Eliciting and Analyzing Domain Concepts
Published in Karen L. McGraw, Karan Harbison, User-Centered Requirements: The Scenario-Based Engineering Process, 2020
Karen L. McGraw, Karan Harbison
Conceptual clustering, a methodology for organizing and summarizing objects or concepts in a domain, is the process of grouping "exemplars" in logical ways (McGraw & Harbison-Briggs, 1989). According to Steep (1987), one way to cluster concepts is to present them in a hierarchy of categories or a tree structure. One way to describe conceptual identifications, clusters, and dependencies is the use of taxonomies, or basic classification systems. Shannon (1980) contended that the validity of any analysis depends on the use of a valid taxonomic model.
Using Boolean factors for the construction of an artificial neural networks
Published in International Journal of General Systems, 2018
Lauraine Tiogning Kueti, Norbert Tsopze, Cezar Mbiethieu, Engelbert Mephu-Nguifo, Laure Pauline Fotso
The optimal factors belong to the set of formal concepts that comes from FCA (Wille 1982). Formal concept analysis is a field of mathematics widely used in data analysis, knowledge representation and information management. Its aim is the representation of data using the notion of concept as consisting of intention and extension and on the organization of the concepts through a conceptual hierarchy (Ganter and Wille 1999). FCA can be understood as conceptual clustering method, which clusters simultaneously objects and their descriptions. There is a growing interest in applications of FCA: data mining, neural networks, software engineering, linguistics, psychology, information retrieval and so on. The initial data to be used are represented in the form of a binary relation on a set of objects and attributes called formal context.