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WishDish: “Recipe Prediction using K-Medoids Clustering Technique for Big Data Analytics”

by Twisha Phirke, Pushkara Dighe, Darshana Rathi, Jayalakshmi Iyer, Nupur Giri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 4
Year of Publication: 2016
Authors: Twisha Phirke, Pushkara Dighe, Darshana Rathi, Jayalakshmi Iyer, Nupur Giri
10.5120/ijca2016912006

Twisha Phirke, Pushkara Dighe, Darshana Rathi, Jayalakshmi Iyer, Nupur Giri . WishDish: “Recipe Prediction using K-Medoids Clustering Technique for Big Data Analytics”. International Journal of Computer Applications. 153, 4 ( Nov 2016), 53-56. DOI=10.5120/ijca2016912006

@article{ 10.5120/ijca2016912006,
author = { Twisha Phirke, Pushkara Dighe, Darshana Rathi, Jayalakshmi Iyer, Nupur Giri },
title = { WishDish: “Recipe Prediction using K-Medoids Clustering Technique for Big Data Analytics” },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 4 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 53-56 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number4/26394-2016912006/ },
doi = { 10.5120/ijca2016912006 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:16.744229+05:30
%A Twisha Phirke
%A Pushkara Dighe
%A Darshana Rathi
%A Jayalakshmi Iyer
%A Nupur Giri
%T WishDish: “Recipe Prediction using K-Medoids Clustering Technique for Big Data Analytics”
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 4
%P 53-56
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The project involves developing a web application called “WishDish” which proves as a one-stop destination for shopping food items, getting recipe suggestions for those food items and planning daily meals for the week on a calendar, all at one place. Recipe suggestions will be made using k-medoids clustering technique on weighted recipes. Aim is to make relevant and distinct recipe suggestions for food items bought every day. A part of this project also involves evaluating performance of the application based on the algorithm used, for different database sizes and accuracy, using R.

References
  1. Mugdha Jain, Chakradhar Verma, “Adapting k-mean for Clustering in Big Data” International Journal of Computer Applications (0975 – 8887) Volume 101– No.1, September 2014.
  2. Sakuntala Gangaraju, “Recipe Suggestion Tool”.
  3. Jeremy Cohen, Robert Sami, Aaron Schild, Spencer Tank, “Recipe Recommendation”- May 2013.
  4. Ji-Rong Wen, Jian-Yun Nie, Hong-Jiang Zhang, “Clustering User Queries of a Search Engine”.
  5. https://grocermax.com
  6. http://www.jamieoliver.com
  7. http://www.tarladalal.com
  8. http://www.sanjeevkapoor.com
  9. https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm
  10. https://stat.ethz.ch/R-manual/R-devel/library/cluster/html/clara.object.html
  11. http://www.fi.muni.cz/~xpelanek/PV254/slides/evaluation.pdf
  12. http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
Index Terms

Computer Science
Information Sciences

Keywords

Big data data analytics k-medoids recommendation system recipe suggestions food shopping food planning meal planning.

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