TY - GEN A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo T1 - Non-TI Clustering in the Context of Social Networks T2 - Procedia Computer Science : The 11th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 3rd International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops N2 - Traditional clustering algorithms like K-medoids and DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, thus, may compromise the quality of resulting clusters. However, there are scenarios, for example in the context of social networks, where TI does not hold but a meaningful clustering is still possible. This paper investigates the consequences of TI violation with respect to different traditional clustering techniques and presents instead a clique guided approach to find meaningful clusters. In this paper, we use the quantum logic-based query language (CQQL) to measure the similarity value between objects instead of a distance function. The contribution of this paper is to propose an approach of non-TI clustering in the context of social network scenario. KW - Clustering KW - Social Network KW - Clique KW - Triangle Inequality Y1 - 2020 U6 - https://doi.org/10.1016/j.procs.2020.03.031 SN - 1877-0509 VL - 170 SP - 1186 EP - 1191 ER - 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 - TY - CHAP A1 - Saha, Sanjit Kumar A1 - Schmitt, Ingo ED - Yu, Han ED - Fang, Fei ED - Guodong, Long ED - Xue, Li ED - Weitong, Chen ED - Jiang, Jing ED - Yue, Lin ED - Bohan, Li T1 - A Relevance Feedback-Based Approach for Non-TI Clustering T2 - Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II N2 - Homogeneity of persons in a social network is based on the similarity of their attributes. Traditional clustering algorithms like hierarchical (agglomerative) clustering or DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, in addition, not all attributes do have the same influence on the network and thus may affect the network and compromise the quality of resulting clusters. We present an adaptive clustering-based quantitative weighting approach that is completely embedded in logic. To facilitate the user interaction with the system, we exploit the concept of relevance feedback. The approach takes user feedback as input to improve the quality of clusters and finds meaningful clusters where TI does not hold. In addition, it has the capability of providing the user alternative possible feedbacks that can be fulfilled. To test the approach, we evaluate a clustering distance regarding an ideal solution. Experiments demonstrate the benefits of our approach. Y1 - 2022 UR - https://www.springerprofessional.de/en/advanced-data-mining-and-applications/20081846?tocPage=2 SN - 978-3-030-95408-6 SN - 978-3-030-95407-9 SP - 381 EP - 393 PB - Springer International Publishing CY - Cham ER -