TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Quantitative Weighting Approach for Non-TI Clustering T2 - Procedia Computer Science N2 - 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. KW - Clustering KW - Social Network KW - Condition Weighting KW - Triangle Inequality KW - Clique Y1 - 2021 UR - https://www.sciencedirect.com/science/article/pii/S1877050921007675 U6 - https://doi.org/10.1016/j.procs.2021.03.119 SN - 1877-0509 VL - 184 SP - 966 EP - 971 ER -