Tags are often used to describe user-generated content on the Web. However, the available Web applications are not incrementally dealing with new tag information, which negatively influences their scalability. Since the cosine similarity between tags represented as co-occurrence vectors is an important aspect of these frameworks, we propose two approaches for an incremental computation of cosin...

Similarity of intuitionistic fuzzy sets (IFSs) is an important measure to indicate the similarity degree between IFSs. Recently, Ye (2011) proposed a similarity measure between IFSs based on the cosine concept. Although this cosine similarity measure has good concept and merit, the measure is not satisfied the definition of a similarity between IFSs and not presented well for analyzing IFS data...

Generalized trapezoidal fuzzy numbers (GTFNs) have been widely applied in uncertain decision-making problems. The similarity between GTFNs plays an important part in solving such problems, while there are some limitations in existing similarity measure methods. Thus, based on the cosine similarity, a novel similarity measure of GTFNs is developed which is combined with the concepts of geometric...

It is vital to select an appropriate distance metric for many learning algorithm. Cosine distance is an efficient metric for measuring the similarity of descriptors in classification task. However, the cosine similarity metric learning (CSML)[1] is not widely used due to the complexity of its formulation and time consuming. In this paper, a Quasi Cosine Similarity Metric Learning (QCSML) is pro...

The degree of similarity or dissimilarity between the objects under study plays an important role. In vector space, especially, the cosine similarity measure is often used in information retrieval, citation analysis, and automatic classification. However, it scarcely deals with trapezoidal fuzzy information and multicriteria decision-making problems. For this purpose, a cosine similarity measur...

In this paper, we present a new methodology for synonym detection based on the combination of global and local distributional similarities of pairs of words. The methodology is evaluated on the noun space of the 50 multiple-choice synonym questions taken from the ESL and reaches 91.30% accuracy using a conditional probabilistic model associated with the cosine similarity measure.