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Quantitative Weighting Approach for Non-TI Clustering

  • To enable users to influence clusters of a social network by their external feedback, we present an adaptive clustering-based quantitative weighting approach. Intrinsically, Persons in a social network are connected and their homogeneity is reflected based on the similarity of their attributes. But all attributes do not have the same influence on the network and thus may affect to form the network and compromise the quality of resulting clusters. The introduced weighting approach is completely embedded in logic and has the capability of assigning query weights to atomic conditions in user interaction. Hence, the presented system supports users by offering an intuitive feedback formulation without deeper knowledge of the underlying attributes of objects. Experiments demonstrate the benefits of our approach.

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Author: Sanjit Kumar Saha, Ingo SchmittORCiDGND
URL:https://www.sciencedirect.com/science/article/pii/S1877050921007675
DOI:https://doi.org/10.1016/j.procs.2021.03.119
ISSN:1877-0509
Title of the source (English):Procedia Computer Science
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2021
Tag:Clique; Clustering; Condition Weighting; Social Network; Triangle Inequality
Volume/Year:184
First Page:966
Last Page:971
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Datenbank- und Informationssysteme
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