Effective and Efficient Algorithms for Flexible Aggregate Similarity Search in High Dimensional Spaces

Document Type

Article

Publication Date

12-1-2015

Abstract

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 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 variant 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.

Identifier

84960156942 (Scopus)

Publication Title

IEEE Transactions on Knowledge and Data Engineering

External Full Text Location

https://doi.org/10.1109/TKDE.2015.2475740

ISSN

10414347

First Page

3258

Last Page

3273

Issue

12

Volume

27

Grant

15H02753

Fund Ref

Japan Society for the Promotion of Science

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