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A Relevance Feedback-Based Approach for Non-TI Clustering

  • 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 distanceHomogeneity 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.show moreshow less

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Metadaten
Author: Sanjit Kumar Saha, Ingo SchmittORCiDGND
URL:https://www.springerprofessional.de/en/advanced-data-mining-and-applications/20081846?tocPage=2
ISBN:978-3-030-95408-6
ISBN:978-3-030-95407-9
Title of the source (English):Advanced Data Mining and Applications, 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II
Publisher:Springer International Publishing
Place of publication:Cham
Editor: Han Yu, Fei Fang, Long Guodong, Li Xue, Chen Weitong, Jing Jiang, Lin Yue, Li Bohan
Document Type:Part of a book (chapter)
Language:English
Year of publication:2022
First Page:381
Last Page:393
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Datenbank- und Informationssysteme
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