The production data-based similarity coefficient versus Jaccard's similarity coefficient

The Production Data-based Similarity Coefficient

This paper compares the performance of Jaccard's similarity coefficient with the producti on data-based simi 1arity coeffi­ cient. A number of machine-component charts taken from the literature or randomly generated are used to form machine-component groups. Then, the sum of intercellular and intracellular material handling costs for each machine-component group is calculated and used as a basi...

Machine Cell Formation Based on a New Similarity Coefficient

One of the designs of cellular manufacturing systems (CMS) requires that a machine population be partitioned into machine cells. Numerous methods are available for clustering machines into machine cells. One method involves using a similarity coefficient. Similarity coefficients between machines are not absolute, and they still need more attention from researchers. Although there are a number o...

Unilateral Jaccard Similarity Coefficient

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various similarity measures are categorized in both syntactic and semantic relationships. In this paper we present a novel similarity, Unilateral Jaccard Similarity Coefficient (uJaccard), which doesn’t only take into consideration the space among two points b...

Application Similarity Coefficient Method to Cellular Manufacturing

Interactions Between Weighting Scheme and Similarity Coefficient in Similarity-Based Virtual Screening

Similarity searching is one of the most common methods for ligand-based virtual screening, and is normally carried out using the Tanimoto coefficient with binary fingerprints. However, a recent study has suggested that it may be less appropriate for use with weighted fingerprints in some circumstances. This paper compares the Tanimoto coefficient with other coefficients, and demonstrates that o...