Cluster-lift method for mapping research activities over a concept tree

Research output: Chapter in Book/Report/Conference proceedingChapter

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The paper builds on the idea by R. Michalski of inferential concept interpretation for knowledge transmutation within a knowledge structure taken here to be a concept tree. We present a method for representing research activities within a research organization by doubly generalizing them. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS). Our cluster-lift method involves two generalization steps: one on the level of individual activities (clustering) and the other on the concept structure level (lifting). Clusters are extracted from the data on similarity between ACM-CCS topics according to the working in the organization. Lifting leads to conceptual generalization of the clusters in terms of "head subjects" on the upper levels of ACM-CCS accompanied by their gaps and offshoots. A real-world example of the representation is provided.
Original languageUnknown
Title of host publicationAdvances in Machine Learning II
EditorsJ. Koronacki, S. Weirzchon, Z. Ras, J. Kacprzyk
Place of PublicationBerlin Heidelberg
PublisherSpringer Verlag
ISBN (Print)978-3-642-05178-4 / 978-3-642-05179-1
Publication statusPublished - 1 Jan 2010

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

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