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.…
Author: | Sanjit Kumar Saha, Ingo SchmittORCiDGND |
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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 |