title: Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines.
abstract: BACKGROUND
  The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results.
  
  
  RESULTS
  In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively.
  
  
  CONCLUSIONS
  The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.|Department of Computer Science, Bogor Agricultural University, Jl. Meranti Wing 20 Level 5 Kampus IPB Dramaga, Bogor, 16680, Indonesia.
authors.forename: Sony Hartono
authors.initials: SH
authors.lastname: Wijaya
authors.affiliation: Department of Statistics, Bogor Agricultural University, Jl. Meranti Wing 22 Level 4 Kampus IPB Dramaga, Bogor, 16680, Indonesia.
authors.forename: Farit Mochamad
authors.initials: FM
authors.lastname: Afendi
authors.affiliation: Tropical Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3, Bogor, 16128, Indonesia.
authors.forename: Irmanida
authors.initials: I
authors.lastname: Batubara
authors.affiliation: Tropical Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3, Bogor, 16128, Indonesia.
authors.forename: Latifah K
authors.initials: LK
authors.lastname: Darusman
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.
authors.forename: Md
authors.initials: M
authors.lastname: Altaf-Ul-Amin
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan. skanaya@gtc.naist.jp.
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Kanaya
mesh_terms: D016000:Cluster Analysis; D029001:Herbal Medicine / Q000379:methods; D007214:Indonesia; D007564:Japan; D010946:Plants, Medicinal / Q000145:classification*; D012372:ROC Curve
keywords: Binary data; Distance metric; Hierarchical clustering; Jamu; Kampo; ROC curve; Similarity measures