Akbaş, Cem EmreBozkurt, AlicanArslan, Musa TunçAslanoğlu, HüseyinÇetin, A. Enis2016-02-082016-02-082014-11http://hdl.handle.net/11693/28674Date of Conference: 1-2 Nov. 2014Conference name: International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector 'product' using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm. As a result, new cosine measure-like similarity measures are normalized by the ℓ1-norms of the vectors. They can be computed using the MapReduce framework. Simulation examples are presented. © 2014 IEEE.EnglishBig dataCosine similarityMapReduceMultiplication-free operatorArtificial intelligenceData handlingInformation analysisVectorsCosine similarity measuresMap-reduceMapreduce frameworksSimilarity measureSimulation exampleVector similarityBig dataConference Paper10.1109/IWCIM.2014.7008798