COMPARISON OF SIMILARITY AND DISSIMILARITY FOR INFORMATION RETRIEVAL

Authors

  • Khin Lay Myint Faculty of Information Science, University of Computer Studies, Hinthada, Myanmar
  • Khin Shin Thant of Information Science, University of Computer Studies, Hinthada, Myanmar
  • Moe Thidar Naing Faculty of Computer Science, University of Computer Studies, Pakokku, Myanmar
  • Hlaing Htake Khaung Tin University of Computer Studies, Hinthada, Myanmar

DOI:

https://doi.org/10.17605/OSF.IO/M7GNT

Keywords:

COMPARISON OF SIMILARITY AND DISSIMILARITY FOR INFORMATION RETRIEVAL

Abstract

v

Downloads

Download data is not yet available.

References

Apparicio P, Abdelmajid M, Riva M, Shearmur R. Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International Journal of Health Geographics.. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans. on Neural Networks, vol. 4, pp. 570-578, July 1993.

https://slidewiki.org/print/1265/data-mining/1280-2/2.

Baroni-Urbani, C., Buser, M.W., (1976), “Similarity of Binary Data”

Shirkhorshidi AS, Aghabozorgi S, Wah TY, Herawan T. Big Data Clustering: A Review Computational Science and Its Applications.

Aczél J, Saaty TL Procedures for synthesizing ratio judgements. J Math Psychol.

Balakrishnan V, Sanghvi LD Distance between populations on the basis of attribute data. Biometrics.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835347/

Downloads

Published

27-06-2020

How to Cite

[1]
Khin Lay Myint, Khin Shin Thant, Moe Thidar Naing, and Hlaing Htake Khaung Tin, “COMPARISON OF SIMILARITY AND DISSIMILARITY FOR INFORMATION RETRIEVAL”, IEJRD - International Multidisciplinary Journal, vol. 5, no. ICIPPS, p. 6, Jun. 2020.