Performance improvement of group-based queries using FCM and GK fuzzy clustering

Abstract

Group nearest-neighbor (GNN) queries are a generalization of nearest-neighbor queries where the goal is to find one or more points from a set of destination points that have the smallest total distance from all query points. In fact, since people are situated at the query points as members of a group, and the perception of people about distance can be different, the classic GNN models cannot be used. On the other hand, more rich and multi-faceted distance models based on type-2 fuzzy logic require heavy computations which makes them difficult to use in realworld applications. In this paper, we propose a method based on fuzzy clustering of destination points that helps to compute the approximate response to GNN query in efficient time. For this purpose, two fuzzy clustering methods are compared using four evaluation criteria. The results show that one of the fuzzy clustering methods provides a high performance improvement while keeping a good quality of approximation in terms of similarity between ideal and approximated response sets. © 2010 Praise Worthy Prize S.r.l.