Constructing and Mapping Fuzzy Thematic Clusters to Higher Ranks in a Taxonomy

• Published in: Knowledge Science, Engineering and Management Vol. 6291; pp. 329 - 340
• Main Authors: Mirkin, Boris, Nascimento, Susana, Fenner, Trevor, Pereira, Luís Moniz
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2010; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Additive Cluster; Fuzzy Cluster; Fuzzy Membership; Spectral Cluster; Thematic Cluster
• ISBN: 9783642152795; 3642152791
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-15280-1_31

We present a novel methodology for mapping a system such as a research department to a related taxonomy in a thematically consistent way. The components of the structure are supplied with fuzzy membership profiles over the taxonomy. Our method generalizes the profiles in two steps: first, by fuzzy clustering, and then by mapping the clusters to higher ranks of the taxonomy. To be specific, we concentrate on the Computer Sciences area represented by the taxonomy of ACM Computing Classification System (ACM-CCS). We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that, in contrast to other methods, involves a number of model-based stopping conditions. The clusters are not necessarily consistent with the taxonomy. This is formalized by a novel method for parsimoniously elevating them to higher ranks of the taxonomy using an original recursive algorithm for minimizing a penalty function that involves “head subjects” on the higher ranks of the taxonomy along with their “gaps” and “offshoots”. An example is given illustrating the method applied to real-world data.