%0 Journal Article
%J Intelligent Systems, IEEE
%D 2008
%T CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior
%A Martinez,V.
%A Simari,G. I
%A Sliva,A.
%A V.S. Subrahmanian
%K (artificial
%K algorithm;action
%K algorithm;behavioural
%K algorithm;CONVEXk-NN
%K BEHAVIOR
%K computing;ontologies
%K CONVEXMerge
%K forecasting;high-dimensional
%K intelligence);
%K metric
%K sciences
%K space;ontology;similarity-based
%K vector;context
%K vector;group
%X A proposed framework for predicting a group's behavior associates two vectors with that group. The context vector tracks aspects of the environment in which the group functions; the action vector tracks the group's previous actions. Given a set of past behaviors consisting of a pair of these vectors and given a query context vector, the goal is to predict the associated action vector. To achieve this goal, two families of algorithms employ vector similarity. CONVEXk _NN algorithms use k-nearest neighbors in high-dimensional metric spaces; CONVEXMerge algorithms look at linear combinations of distances of the query vector from context vectors. Compared to past prediction algorithms, these algorithms are extremely fast. Moreover, experiments on real-world data sets show that the algorithms are highly accurate, predicting actions with well over 95-percent accuracy.
%B Intelligent Systems, IEEE
%V 23
%P 51 - 57
%8 2008/08//july
%@ 1541-1672
%G eng
%N 4
%R 10.1109/MIS.2008.62