A Relevance Feedback-Based Approach for Non-TI Clustering

• Published in: Advanced Data Mining and Applications Vol. 13088; pp. 381 - 393
• Main Authors: Saha, Sanjit Kumar, Schmitt, Ingo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Clique; Clustering distance; Quantitative weighting; Relevance feedback; Similarity measure; Social network clustering
• ISBN: 9783030954079; 3030954072
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-95408-6_29

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.