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Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery

Basavaraj A. Goudannavar1 , Prashant Bhat2

  1. Department of Computer Science, Rani Channamma University, Belagavi-591156, Karnataka, India.
  2. Department of Business Analytics/Data Science, Chris Institute of Management, Lavasa-412112, Pune, Maharashtra, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 316-320, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.316320

Online published on Mar 30, 2018

Copyright © Basavaraj A. Goudannavar, Prashant Bhat . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Basavaraj A. Goudannavar, Prashant Bhat, “Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.316-320, 2018.

MLA Style Citation: Basavaraj A. Goudannavar, Prashant Bhat "Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery." International Journal of Computer Sciences and Engineering 6.3 (2018): 316-320.

APA Style Citation: Basavaraj A. Goudannavar, Prashant Bhat, (2018). Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery. International Journal of Computer Sciences and Engineering, 6(3), 316-320.

BibTex Style Citation:
@article{Goudannavar_2018,
author = { Basavaraj A. Goudannavar, Prashant Bhat},
title = {Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2018},
volume = {6},
Issue = {3},
month = {3},
year = {2018},
issn = {2347-2693},
pages = {316-320},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1801},
doi = {https://doi.org/10.26438/ijcse/v6i3.316320}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.316320}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1801
TI - Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery
T2 - International Journal of Computer Sciences and Engineering
AU - Basavaraj A. Goudannavar, Prashant Bhat
PY - 2018
DA - 2018/03/30
PB - IJCSE, Indore, INDIA
SP - 316-320
IS - 3
VL - 6
SN - 2347-2693
ER -

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Abstract

Frequent sets play a crucial role in many Data Mining tasks that try to find interesting patterns from databases, such as correlations, association rules, classification and clustering. The Association Rules is one of the most used functions in data mining. The method is used both database researchers and data mining users. In this article, association rule mining algorithms are discussed and demonstrated. Mining Associate rule algorithm that search for approximate strong association rules from multimedia databases. The Apriori-like sequential pattern mining approach based on candidate generates-and test can also be explored by mapping a sequence multimedia database into vertical data format. This approach is useful to finding frequent itemsets, which probabilistic frequent itemsets based on possible datasets.

Key-Words / Index Term

Web Multimedia Mining, Association rule, Frequent itemsets, Knowledge discovery

References

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