@inbook{fd433e3febac46c8af03caea6fba8cca,
title = "Cluster-lift method for mapping research activities over a concept tree",
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.",
author = "Almeida, {Susana Maria dos Santos Nascimento M. de}",
year = "2010",
month = jan,
day = "1",
language = "Unknown",
isbn = "978-3-642-05178-4 / 978-3-642-05179-1",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
number = "263",
pages = "245--257",
editor = "J. Koronacki and S. Weirzchon and Z. Ras and J. Kacprzyk",
booktitle = "Advances in Machine Learning II",
address = "Germany",
edition = "1st",
}