Flexible Aggregate Similarity Search in High-Dimensional Data Sets

Numerous applications in different fields, such as spatial databases, multimedia databases, data mining and recommender systems, may benefit from efficient and effective aggregate similarity search, also known as aggregate nearest neighbor (AggNN) search. Given a group of query objects Q, the goal of AggNN is to retrieve the k most similar objects from the database, where the underlying similarity measure is defined as an aggregation (usually sum, avg or max) of the distances between the retrieved objects and every query object in Q. Recently, the problem was generalized so as to retrieve the k objects which are most similar to a fixed proportion of the elements of Q. This variant of aggregate similarity search is referred to as 'flexible AggNN', or FANN. In this work, we propose two approximation algorithms, one for the sum and avg variants of FANN, and the other for the max variant. Extensive experiments are provided showing that, relative to state-of-the-art approaches (both exact and approximate), our algorithms produce query results with good accuracy, while at the same time being very efficient - even for real datasets of very high dimension.