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Reseach Article

Comparative Study of Recommendation Algorithms and Systems using WEKA

by Lokesh S. Katore, J.s.umale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 3
Year of Publication: 2015
Authors: Lokesh S. Katore, J.s.umale
10.5120/19295-0731

Lokesh S. Katore, J.s.umale . Comparative Study of Recommendation Algorithms and Systems using WEKA. International Journal of Computer Applications. 110, 3 ( January 2015), 14-17. DOI=10.5120/19295-0731

@article{ 10.5120/19295-0731,
author = { Lokesh S. Katore, J.s.umale },
title = { Comparative Study of Recommendation Algorithms and Systems using WEKA },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 3 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number3/19295-0731/ },
doi = { 10.5120/19295-0731 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:23.564678+05:30
%A Lokesh S. Katore
%A J.s.umale
%T Comparative Study of Recommendation Algorithms and Systems using WEKA
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 3
%P 14-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommendation systems now days are the heart of success stories for business and optimization of resources. The accurate prediction of business decision accurately depends on heuristic algorithms used for analytics. Classical algorithms used for the data mining find their utility to perform with the new challenges considering key factors for improvement. This paper presents the performance of the specific algorithms of the data mining class in view to observe their suitability for recommender systems.

References
  1. Naive Bayes text classification (http://nlp. stanford. edu/IR-book/html/htmledition/naive bayes-text-classification-1. html) accessed on March 14. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  2. Dataset of Liver, Bank and dermatology (www. repository. seasr. org/Datasets/UCI/arff/) accessed on March 14.
  3. Information about Weka and its application (www. cs. waikato. ac. nz/ml/weka).
  4. K*: An Instance-based Learner Using an Entropic Distance Measure Authors John G. Cleary, Leonard E. Trigg, Dept. of Computer Science, University of Waikato,NewZealand. email:{jcleary,trigg}@waikato. ac. nz.
  5. Tina R. Patil, Mrs. S. S. Sherekar ,Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification
  6. B Imbalanced Data Set Learning with Synthetic Examples, Authors Benjamin X. Wang and Nathalie Japkowicz.
  7. University of Waikato,( www. waikato. ac. nz. ) accessed on March 14.
  8. S Analysis of Liver Disorder Using Data mining Algorithm, Global journal of computer science and technology, Vol. 10 Issue 14 (Ver. 1. 0) November 2010 P a g e | 48
  9. https://www. princeton. edu/~achaney/tmve/wiki100k/docs/Naive_Bayes_classifier. html naive bayes.
  10. Dr. Neeraj Bhargava, Girja Sharma, Dr. Ritu Bhargava, Manish Mathuria; International Journal of Advanced Research in Computer Science and Software Engineering
  11. Ms S. Vijayarani ,Ms M. Muthulakshmi; Comparative Analysis of Bayes and Lazy Classification Algorithms.
  12. Sanidhya Painuli,M. Elangovan, V. Sugumaran; Tool condition monitoring using K-star algorithm
Index Terms

Computer Science
Information Sciences

Keywords

Data mining WEKA J48 Naïve Bayes Simple Cart K Star Resample SMOTE.