We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Novel Approach for Finding Frequent Itemsets done by Comparison based Technique

by Meghna Utmal, Shailendra Chourasia, Rashmi Vishwakarma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 9
Year of Publication: 2012
Authors: Meghna Utmal, Shailendra Chourasia, Rashmi Vishwakarma
10.5120/6292-8488

Meghna Utmal, Shailendra Chourasia, Rashmi Vishwakarma . A Novel Approach for Finding Frequent Itemsets done by Comparison based Technique. International Journal of Computer Applications. 44, 9 ( April 2012), 23-27. DOI=10.5120/6292-8488

@article{ 10.5120/6292-8488,
author = { Meghna Utmal, Shailendra Chourasia, Rashmi Vishwakarma },
title = { A Novel Approach for Finding Frequent Itemsets done by Comparison based Technique },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 9 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number9/6292-8488/ },
doi = { 10.5120/6292-8488 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:06.724103+05:30
%A Meghna Utmal
%A Shailendra Chourasia
%A Rashmi Vishwakarma
%T A Novel Approach for Finding Frequent Itemsets done by Comparison based Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 9
%P 23-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this paper, we develop a new technique for more efficient pattern mining. Our method find frequent 1-itemset and then uses the heap tree sorting we are generating frequent patterns, so that many. We present efficient techniques to implement the new approach.

References
  1. Afrati FN, Gionis A, Mannila H (2004) Approximating a collection of frequent sets. In: Proceedings of the 2004 ACM SIGKDD international conference knowledge discovery in databases (KDD'04), Seattle,WA, pp 12–19.
  2. Agarwal R, Aggarwal CC, Prasad VVV (2001) A tree projection algorithm for generation of frequent itemsets. J Parallel Distribut Comput 61:350–371.
  3. Aggarwal CC, Yu PS (1998) A new framework for itemset generation. In: Proceedings of the 1998 ACM symposium on principles of database systems (PODS'98), Seattle,WA, pp 18–24.
  4. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the 1998 ACM-SIGMOD international conference on management of data (SIGMOD'98), Seattle, WA, pp 94–105.
  5. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993ACM-SIGMODinternational conference on management of data (SIGMOD'93), Washington, DC, pp 207–216.
  6. Agrawal R, Shafer JC (1996) Parallel mining of association rules: design, implementation, and experience. IEEE Trans Knowl Data Eng 8:962–969.
  7. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 1994 international conference on very large data bases (VLDB'94), Santiago, Chile, pp 487–499.
  8. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 1995 international conference on data engineering (ICDE'95), Taipei, Taiwan, pp 3–14.
  9. Ahmed KM, El-Makky NM, Taha Y (2000) A note on "beyond market basket: generalizing association rules to correlations". SIGKDD Explorations 1:46–48.
  10. AsaiT, AbeK,Kawasoe S, ArimuraH, SatamotoH,Arikawa S (2002) Efficient substructure discovery from large semi-structured data. In: Proceedings of the 2002 SIAM international conference on data mining (SDM'02), Arlington, VA, pp 158–174.
  11. Aumann Y, Lindell Y (1999) A statistical theory for quantitative association rules. In: Proceeding of the 1999 international conference on knowledge discovery and data mining (KDD'99), San Diego, CA, pp 261–270.
  12. Bayardo RJ (1998) Efficiently mining long patterns from databases. In: Proceeding of the 1998 ACM-SIGMOD international conference on management of data (SIGMOD'98), Seattle,WA, pp 85–93
  13. Beil F, EsterM, Xu X (2002) Frequent term-based text clustering. In: Proceeding of the 2002 ACM SIGKDD international conference on knowledge discovery in databases (KDD'02), Edmonton, Canada, pp 436–442.
  14. Bettini C, SeanWang X, Jajodia S (1998) Mining temporal relationships with multiple granularities in time sequences. Bull Tech Committee Data Eng 21:32–38.
  15. Beyer K, RamakrishnanR(1999) Bottom-up computation of sparse and iceberg cubes. In: Proceeding of the 1999ACM-SIGMODinternational conference on management of data (SIGMOD'99), Philadelphia, PA, pp 359–370.
  16. Blanchard J, Guillet F, Gras R, Briand H (2005) Using information-theoretic measures to assess association rule interestingness. In: Proceeding of the 2005 international conference on data mining (ICDM'05), Houston, TX, pp 66–73.
  17. Frequent pattern mining: current status and future directions Bonchi F, Giannotti F, Mazzanti A, Pedreschi D (2003) Exante: anticipated data reduction in constrained pattern mining. In: Proceeding of the 7th European conference on principles and pratice of knowledge discovery in databases (PKDD'03), pp 59–70.
  18. Bonchi F, Lucchese C (2004) On closed constrained frequent pattern mining. In: Proceeding of the 2004 international conference on data mining (ICDM'04), Brighton, UK, pp 35–42
  19. Borgelt C, Berthold MR (2002) Mining molecular fragments: finding relevant substructures of molecules. In: Proceeding of the 2002 international conference on data mining (ICDM'02), Maebashi, Japan, pp 211–218.
  20. Brin S, Motwani R, Silverstein C (1997) Beyond market basket: generalizing association rules to correlations. In: Proceeding of the 1997 ACM-SIGMOD international conference on management of data (SIGMOD'97), Tucson, AZ, pp 265–276.
  21. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket analysis. In: Proceeding of the 1997 ACM-SIGMOD international conference on management of data (SIGMOD'97), Tucson, AZ, pp 255–264.
  22. Bucila C, Gehrke J, Kifer D, White W (2003) DualMiner: a dual-pruning algorithm for itemsets with constraints. Data Min knowl discov 7:241–272.
  23. Burdick D, Calimlim M, Gehrke J (2001) MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proceeding of the 2001 international conference on data engineering (ICDE'01), Heidelberg, Germany, pp 443–452.
  24. Calders T,Goethals B (2002) Mining all non-derivable frequent itemsets. In: Proceeding of the 2002 European conference on principles and pratice of knowledge discovery in databases (PKDD'02), Helsinki, Finland, pp 74–85.
  25. Calders T, Goethals B (2005) Depth-first non-derivable itemset mining. In: Proceeding of the 2005 SIAM international conference on data mining (SDM'05), Newport Beach, CA, pp 250–261.
  26. Cao H, Mamoulis N, Cheung DW (2005) Mining frequent spatio-temporal sequential patterns. In: Proceeding of the 2005 international conference on data mining (ICDM'05), Houston, TX, pp 82–89.
  27. Zaki MJ (2002) Efficiently mining frequent trees in a forest. In: Proceeding of the 2002 ACM SIGKDD international conference on knowledge discovery in databases (KDD'02), Edmonton, Canada, pp 71–80
  28. Zaki MJ,Hsiao CJ (2002) CHARM: an efficient algorithm for closed itemset mining. In: Proceeding of the 2002SIAMinternational conference on data mining (SDM'02),Arlington,VA, pp 457–473.
  29. Zaki MJ, Lesh N,OgiharaM(1998) PLANMINE: sequencemining for plan failures. In: Proceeding of the 1998 international conference on knowledge discovery and data mining (KDD'98), New York, NY, pp 369–373.
  30. Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997) Parallel algorithm for discovery of association rules. data mining knowl discov, 1:343–374.
  31. Zhang X, Mamoulis N, Cheung DW, Shou Y (2004) Fast mining of spatial collocations. In: Proceeding of the 2004 ACM SIGKDD international conference on knowledge discovery in databases (KDD'04), Seattle,WA, pp 384–393.
  32. Zhang H, Padmanabhan B, Tuzhilin A(2004) On the discovery of significant statistical quantitative rules. In: Proceeding of the 2004 international conference on knowledge discovery and data mining (KDD'04), Seattle,WA, pp 374–383.
  33. Zhu F, Yan X, Han J, Yu PS, Cheng H (2007) Mining colossal frequent patterns by core pattern fusion. In: Proceeding of the 2007 international conference on data engineering (ICDE'07).
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Pattern Mining Maxheap Data Mining Data Structure

Powered by PhDFocusTM