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

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... th 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. Abstract 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.