Many data mining algorithms developed recently are based on inductive learning methods. Very few are based on similarity-based learning. However, similarity-based learning accrues advantages, such as simple representations for concept descriptions, low incremental learning costs, small storage requirements, etc. We present a similarity-based learning method from databases in the context of roug...

We explore the structure of non-redundant and minimal sets consisting of graded if-then rules. The rules serve as graded attribute implications in object-attribute incidence data and as similarity-based functional dependencies in a similarity-based generalization of the relational model of data. Based on our observations, we derive a polynomial-time algorithm which transforms a given finite set...

This paper presents a fuzzy, non-linear similarity measure designed for a clinical casebased reasoning system in radiotherapy treatment planning. The developed fuzzy similarity measure takes into account the distribution of attribute similarity values in the case base to ensure that the numerical values of the similarity between individual attributes are comparable and can be combined to give t...

Similarity measure is an important tool to measure the degree of resemblance between two intuitionistic fuzzy sets. In this paper, in order to overcome the counter-intuitive in some cases, a new similarity measure of intuitionistic fuzzy sets is constructed and successively applied in pattern recognition and medical diagnosis. Based on the proposed similarity measure, a new decision making meth...

MOTIVATION A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust represent...

The objective of the study is to present cosine similarity measure based multi-attribute decision making under neutrosophic environment. The assesments of alternatives over the attributes are expressed with trapezoidal fuzzy neutrosophic numbers in which the three independent components namely, truth-membership degree (T), indeterminacy-membership degree (I) and falsity-membership degree (F) ar...

Most of data in Multi-attribute decision making (MADM) problems are changeable rather than constant and stable. Therefore, sensitivity analysis after problem solving can effectively contribute to making accurate decisions. In this paper, we offer a new method for sensitivity analysis in multi-attribute decision making problems in which if the weights of one attribute changes, then we can dete...

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here, the community structure of protein sequence similarity networks is determined with a new...

To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean sets, intuitionistic and sets. Applications for similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation the weights criteria multi-criteria decision-making problem heavily relies on entropy measu...

In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ ...