Clustering is one of the most interesting and important tool for research in data mining and other disciplines. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the appropriateness of the similarity measure between the data in which the similarity can be computed. This pap...

The linguistic neutrosophic numbers (LNNs) can express the truth, indeterminacy, and falsity degrees independently by three linguistic variables. Hence, they are an effective tool for describing indeterminate linguistic information under linguistic decision-making environments. Similarity measures are usual tools in decision-making problems. However, existing cosine similarity measures have bee...

In this proof-of-concept study we use standard cosine similarity measure to calculate the semantic similarity between two pieces of text – the citing document and the cited text. Three subject matter experts then evaluate the citing and the cited text based on the cosine score to give their judgement on the semantic similarity between the two pieces of text.

We show how to consider similarity between features for calculation of similarity of objects in the Vec­ tor Space Model (VSM) for machine learning algorithms and other classes of methods that involve similarity be­ tween objects. Unlike LSA, we assume that similarity between features is known (say, from a synonym dictio­ nary) and does not need to be learned from the data. We call the proposed...

Similarity measure is an important tool in pattern recognition and fault diagnosis. This paper proposes two cotangent similarity measures for single-valued neutrosophic sets (SVNSs) based on cotangent function. Then, the weighted cotangent similarity measures are introduced by considering the importance of each element. Moreover, by the comparison between the cotangent similaritymeasures of SVN...

Deep supervised hashing takes prominent advantages of low storage cost, high computational efficiency and good retrieval performance, which draws attention in the field large-scale image retrieval. However, similarity-preserving, quantization errors imbalanced data are still great challenges deep hashing. This paper proposes a pairwise similarity-preserving scheme to handle aforementioned probl...

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive negative messages as well are circulated between those users. Users form special with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose framework recomme...

Corresponding Author: Jitendra Nath Singh Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India Email: [email protected] Abstract: Vector space model allows computing a continuous degree of similarity between queries and retrieved documents and then ranks the documents in increasing order of cosine (similarity) value. It computes cosine or similarity value us...

In this paper we consider the problem of job recommendation, suggesting suitable jobs to users based on their profiles. We compare a baseline method treating users and jobs as documents, where suitability is measured using cosine similarity, with a model that incorporates job transitions trained on the career progressions of a set of users. We show that the job transition model outperforms cosi...

We propose a new similarity measure between texts which, contrary to the current state-of-the-art approaches, takes a global view of the texts to be compared. We have implemented a tool to compute our textual distance and conducted experiments on several corpuses of texts. The experiments show that our methods can reliably identify different global types of texts.