CFP last date
20 January 2025
Reseach Article

A Survey on Problems and Solutions of Frequent Pattern Mining with the use of Pre-Processing Techniques

by Anupriya Babbar, Anju Singh, Divakar Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 1
Year of Publication: 2014
Authors: Anupriya Babbar, Anju Singh, Divakar Singh
10.5120/16559-4125

Anupriya Babbar, Anju Singh, Divakar Singh . A Survey on Problems and Solutions of Frequent Pattern Mining with the use of Pre-Processing Techniques. International Journal of Computer Applications. 95, 1 ( June 2014), 23-28. DOI=10.5120/16559-4125

@article{ 10.5120/16559-4125,
author = { Anupriya Babbar, Anju Singh, Divakar Singh },
title = { A Survey on Problems and Solutions of Frequent Pattern Mining with the use of Pre-Processing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 1 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number1/16559-4125/ },
doi = { 10.5120/16559-4125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:18.727814+05:30
%A Anupriya Babbar
%A Anju Singh
%A Divakar Singh
%T A Survey on Problems and Solutions of Frequent Pattern Mining with the use of Pre-Processing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 1
%P 23-28
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent pattern mining is a researched area which is used for extracting interesting associations and correlations among item sets in transactional and relational database. Many algorithms of frequent pattern mining is been devised ranging from efficient and scalable algorithms in transactional database to numerous research frontiers and their wide applications. Many researches been done into FPM [1], but there are still several optimizations are required, so that FPM can be used more efficiently in data mining applications. For optimization purpose in many mining techniques data pre-processing plays an important role in reducing data size and also in lessening the time taken in database scans. This paper is a detailed study of problems and solutions of FPM techniques incorporated with pre-processing techniques. The intent of this paper is to summarize all major problems of FPM and their solutions. From this survey, it concludes that if FPM methods are merged with pre-processing techniques will produce results with better performance.

References
  1. Thashmee Karunaratne, "Is Frequent Pattern Mining Useful In Building Predictive Models?" Stockholm University, Forum 100, Se-164 40 Kista, Sweden.
  2. Jiawei Han , Hong Cheng , Dong Xin Xifeng Yan, "Frequent Pattern Mining: Current Status And Future Directions" Springer Science+Business Media, Llc 2007.
  3. Norwati Mustapha, Mohammad-Hossein Nadimi-Shahraki, Ali B Mamat, Md. Nasir B Sulaiman "A Numerical Method for Frequent Patterns Mining Journal of Theoretical And Applied Information Technology". Journal of Theoretical and Applied Information Technology, 2009.
  4. Renáta Iváncsy, István Vajk, "Frequent Pattern Mining In Web Log Data" Acta Polytechnica Hungarica Vol. 3, No. 1, 2006.
  5. Bart Goethals, "Survey on Frequent Pattern Mining" Journal On Computer Science And Engineering 2010.
  6. Jiawei Han ,Jian Pei , Iwen Yin , "Mining Frequent Patterns Without Candidate Generation: A Frequent-Pattern Tree Approach", Received May 21, 2000; Revised April 21, 2001.
  7. Qiankun Zhao,Sourav S. Bhowmick, "Association Rule Mining: A Survey", Technical Report, Cais, Nanyang Technological University, Singapore, No. 2003116 , 2003.
  8. Bart Goethals, "Memory issues in frequent item set mining" SAC'04, March 14–17, 2004.
  9. Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M-C, " Prefixspan: Mining Sequential Patterns Efficiently By Prefix-Projected Pattern Growth". In: Proceeding Of the 2001International Conference on Data Engineering (ICDE'01), Heidelberg, Germany, 2011.
  10. Wang J, Han J, Pei J , " CLOSET+: Searching For The Best Strategies For Mining Frequent Closed Itemsets". In: Proceeding Of the 2003 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03), Washington, DC, Pp 236–245, 2003.
  11. Han J, Pei J, Yin Y , " Mining Frequent Patterns without Candidate Generation". In: Proceeding Of the 2000 ACM-SIGMOD International Conference on Management of Data (SIGMOD'00), Dallas, TX, Pp 1–12, 2000.
  12. Liu J, Paulsen S, Sun X, Wang W, Nobel A, Prins J , " Mining Approximate Frequent Itemsets In The Presence Of Noise: Algorithm and Analysis". In: Proceeding Of the 2006 SIAM International Conference on Data Mining (SDM'06), Bethesda 2006.
  13. Nadimi-Shahraki M. H. , N. Mustapha, M. NSulaiman, and A. Mamat, "Incremental Updating of Frequent Pattern: Basic Algorithms", Proceedings of the secondInternational Conference on InformationSystems Technology and Management (ICISTM 08), pp. 145-148, 2008. D, Pp 405–416, 2006.
  14. Sotiris-Kotsiantis,Dimitris, "Association Rules Mining: A Recent Overview", GESTS International Transactions on Computer Science and Engineering, Vol. 32 (1), 2006
  15. Gosta Grahne and Jianfei Zhu," Efficiently sing Prefix-trees in Mining Frequent Itemsets", on ordia University Montreal,Canada,2002.
  16. Agrawal Rakesh, Imilienski T. , and Swami Arun. "Mining association rules between sets of items in large datasets", SIGMOD, 207-216, 1993.
  17. Cheung David W. , Lee S. D. , and Kao Benjamin. "A General Incremental Technique for Maintaining Discovered Association Rules", Proc. International Conference On Database Systems For Advanced Applications, April 1997
  18. Hipp Jochen, Güntzer Ulrich, and Nakhaeizadeh Gholamreza, " Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches", 159-168, Lyon, France, September 2000.
  19. Lee Chang Hung, Lin Cheng Ru, and Chen Ming Syan, " Sliding Window Filtering: An Efficient Method for incremental Mining on a Time-Variant Database", Proceedings of 10th International Conference on Information and Knowledge Management, 263-270, November 2001.
  20. Pei Jian, Han Jiawei, Nishio Shojiro, Tang Shiwei, and Yang Dongqing, "H-Mine: Hyper- Structure Mining of Frequent Patterns in Large Databases", Proc. 2001 Int. Conf. on Data Mining, San Jose, CA, November 2001.
  21. Savasere Ashok, Omiecinski Edward, and Navathe Shamkant. "An Efficient Algorithm for Mining Association Rules in Large Databases", Proceedings of the Very Large Data Base Conference, September 1995.
  22. Zaïane Osmar R. and Oliveira Stanley R. M. "Privacy preserving frequent itemset mining", Workshop on Privacy, Security, and Data Mining, in conjunction with the IEEE International Conference on Data Mining, Japan, December 2002.
  23. M. Houtsma and A. Swami, "Set-oriented data mining in relational databases", Data Knowl. Eng. ,245–262, 1995.
  24. Han & Kamber ,"Data Pre-processing & Mining Algorithm", 3 edition ISCE,2001.
  25. Agrawal, Rakesh and Ramakrishnan Srikant, "Fast Algorithms for Mining & Preprocessing Assosiation Rules", Proceedings of the 20th VLDB Conference,Santiago, Chile (1994).
  26. Salleb, Ansaf and Christel Vrain, "An Application of Assosiation Knowledge Discovery and Data Mining" (PKDD) 2000 , LNAI 1910, pp. 613-618, Springer Verlag (2000).
  27. Agrawal, R. , and Psaila, G. " Active Data Mining" In Proceedings on Knowledge Discovery and Data Mining (KDD -95), 3–8. Menlo Park, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Pattern Mining Maximal frequent pattern Data Pre-Processing