@article {17758,
title = {CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior},
journal = {Intelligent Systems, IEEE},
volume = {23},
year = {2008},
month = {2008/08//july},
pages = {51 - 57},
abstract = {A proposed framework for predicting a group{\textquoteright}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{\textquoteright}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.},
keywords = {(artificial, algorithm;action, algorithm;behavioural, algorithm;CONVEXk-NN, BEHAVIOR, computing;ontologies, CONVEXMerge, forecasting;high-dimensional, intelligence);, metric, sciences, space;ontology;similarity-based, vector;context, vector;group},
isbn = {1541-1672},
doi = {10.1109/MIS.2008.62},
author = {Martinez,V. and Simari,G. I and Sliva,A. and V.S. Subrahmanian}
}