@phdthesis{Saha2023, author = {Saha, Sanjit Kumar}, title = {A relevance feedback approach for social network clustering in the context of triangle inequality violations}, doi = {10.26127/BTUOpen-6519}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-65199}, school = {BTU Cottbus - Senftenberg}, year = {2023}, abstract = {People in a social network are connected, and their homogeneity is reflected by the similarity of their attributes. For effective clustering, the similarities among people within a cluster must be much higher than the similarities between different clusters. Traditional clustering algorithms like hierarchical (agglomerative) or k-medoids take distances between objects as input and find clusters of objects. The distance functions used should comply with the triangle inequality (TI) property, but sometimes this property may be violated, thus negatively impacting the quality of the generated clusters. However, in social networks, meaningful clusters can be found even though TI violates. One possibility is a quantum-logic-based clique-guided non-TI clustering approach. The commuting quantum query language (CQQL) is the base for this approach. The CQQL allows the formulation of queries that incorporate both Boolean and similarity conditions. It calculates the similarity value between two objects. Furthermore, attributes may not have equal impact on similarity and affect the resulting clusters. CQQL incorporates weights to express the varying importance of sub-conditions in a query while preserving consistency with Boolean algebra. This enables personalization of results through relevance feedback (RF). The main challenge of comparing clusterings is that there is no ground truth data. In such situations, human-generated gold standard clustering can be used. The question is how to compare the performance of clusterings. A noteworthy technique involves counting the pairs of objects that are grouped identically in both clusterings. By doing so, a clustering distance is calculated that measures the dissimilarity. To validate the non-TI clustering approach, experiments are conducted on social networks of different sizes. Three central questions are addressed by the experiments mentioned: first, is it possible to find meaningful clusters even though TI violates; second, how does a user interact with the system to provide feedback based on their needs; and third, how fast do the detected clusters based on the proposed approach converge to the ideal solution? To sum up, the experiments' objective is to demonstrate the validity of a theoretical approach. The research findings presented here provide sufficient evidence for detecting meaningful clusters based on user interaction. Furthermore, the experiments clearly demonstrate that the non-TI clustering approach can be used as an RF technique in clustering.}, subject = {Social network clustering; CQQL; Relevance feedback; Non-TI clustering; Clustering distance; Clustering sozialer Netzwerke; Relevanz-Feedback; Clustering-Abstand; Social media; Online-Community; Cluster-Analyse; Relevanz-Feedback}, language = {en} }