Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery
Basavaraj A. Goudannavar1 , Prashant Bhat2
- Department of Computer Science, Rani Channamma University, Belagavi-591156, Karnataka, India.
- 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
[1] Agrawal, R., Imielinski, T., Swami, “A.: Mining association rules between sets of items in large databases”,
[2] Acm SIGKDD Explorations Newsletter, Volume 22, Issue 2, June 1993, ISBN: 0-89791-592-5.
[3] Bart Goethals, Mohammed J. Zaki, “Advances in Frequent Itemset Mining Implementations Report on FIMI’03”, ACM SIGKDD Explorations Newsletter, Volume 6, Issue 1, June 2004, ISSN: 1931-0145.
[4] Michael Hahsler, Bettina Grun and Kurt Hornik, “arules – A Computational Environment for Mining Association Rules and Frequent Item Sets”, Journal of Statistical Software, Volume 14, Issue 15,October 2005,.
[5] ChenGang “MediaInfo extractor – A Tool for Media Data Mining”, 2011. http://mediaarea.net/en/MediaInfo.
[6] Jyothsna R. Nayak and Diane J. Cook, “Approximate Association Rule Mining”, FLAIRS-01 Proceedings. 2001, AAAI (www.aaai.org).
[7] Syed Khairuzzaman Tanbeer , Chowdhury , Farhan Ahmed and Byeong-Soo Jeong , “Parallel and Distributed Algorithms for FP mining in large Databases”, IETE Technical Review Vol 26, Issue 1 , pp 55-65, Jan 2009.
[8] Pradeep Chouksey* Juhi Singh, R.S. Thakur and R.C. Jain, “Frequent Pattern Mining using Candidate Generation approach with Single Scan of Database”, Symposium on Progress in Information & Communication Technology 2009
[9] Ms. Manali Rajeev Raut, Ms. Hemlata Dakhore, “Association Rule Mining in Horizontally Distributed Databases”, Manali Rajeev Raut et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (6) , 2014, 7540-7544
[10] Zhi Liu,Tianhong Sunand Guoming Sang," An Algorithm of Association Rules Mining in Large Databases Based on Sampling ", International Journal of Database Theory and Application Vol.6, No.6 , 2013.
[11] Renáta Iváncsy, István Vajk’, “Frequent Pattern Mining in Web Log Data”, Acta Polytechnica Hungarica Vol. 3, No. 1, 2006.
[12] S. M. Fakhrahmad1 And Gh. Dastghaibyfard2, “An Efficient Frequent Pattern Mining Method and its Parallelization in Transactional Databases”, Journal Of Information Science And Engineering 27, 511-525 (2011).
[13] Siddu P. Algur1, Basavaraj A. Goudannavar2*,“Web Multimedia Mining: Metadata Based Classification and Analysis of Web Multimedia”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) November-2015, pp. 324-330 ISSN: 2277-128X