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

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

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
Pages245-257
Edition1st
ISBN (Print)978-3-642-05178-4 / 978-3-642-05179-1
Publication statusPublished - 1 Jan 2010

Publication series

NameStudies in Computational Intelligence
PublisherSpringer-Verlag
Number263
ISSN (Print)1860-949X

Cite this

Almeida, S. M. D. S. N. M. D. (2010). Cluster-lift method for mapping research activities over a concept tree. In J. Koronacki, S. Weirzchon, Z. Ras, & J. Kacprzyk (Eds.), Advances in Machine Learning II (1st ed., pp. 245-257). (Studies in Computational Intelligence; No. 263). Springer-Verlag. http://www.springerlink.com/content/y470647665588127/