Classification is a common task in Machine Learning and Data Mining. Jumping Emerging Patterns have been applied for classification in different contexts with good results and the advantage of to be easily understandable for users. In this work we propose the use of cosine similarity measure to select the patterns which will be used to predict the classes in the classification process. Two vers...

Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Ba...

In this paper, we define a rough cosine similarity measure between two rough neutrosophic sets. The notions of rough neutrosophic sets (RNS) will be used as vector representations in 3D-vector space. The rating of all elements in RNS is expressed with the upper and lower approximation operator and the pair of neutrosophic sets which are characterized by truth-membership degree, indeterminacy-me...

In this paper, we define a new cosine similarity between two interval valued neutrosophic sets based on Bhattacharya’s distance [19]. The notions of interval valued neutrosophic sets (IVNS, for short) will be used as vector representations in 3D-vector space. Based on the comparative analysis of the existing similarity measures for IVNS, we find that our proposed similarity measure is better an...

Measuring text similarity has been studied for a long time due to its importance in many applications in natural language processing and related areas such as Web-based document searching. One such possible application which is investigated in this paper is determining the similarity between course descriptions of the same subject for credit transfer among various universities or similar academ...

Finding similar users in social communities is often challenging, especially in the presence of sparse data or when working with heterogeneous or specialized domains. When computing semantic similarity among users it is desirable to have a measure which allows to compare users w.r.t. any concept in the domain. We propose such a technique which reduces the problems caused by data sparsity, espec...

This study proposed a new method about clustering in documents. Clustering is a very powerful data mining technique for topic discovery from documents. In document clustering, it must be more similarity between intra-document and less similarity between intra-document of two clusters. The cosine function measures the similarity of two documents. When the clusters are not well separated, partiti...

The goal of this project was to recommend songs to users based solely on their listening histories, with no information about the music. We applied various Collaborative Filtering methods to achieve this: user-user neighborhood models, item-item neighborhood models and latent factor models. We achieved the best results with item-item cosine similarity. The code for this project can be found here.

This is a summary of our participation in the TRECVID 2016 video hyperlinking task (LNK). We submitted four runs in total. A baseline system combined on established vectorspace text indexing and cosine similarity. Our other runs explored the use of distributed word representations in combination with fine-grained inter-segment text similarity measures.