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

Data Mining Considering the Instances of Item-Sets

by Aman Raj, Pratik Singh, Debdutta Chatterjee
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 6
Year of Publication: 2012
Authors: Aman Raj, Pratik Singh, Debdutta Chatterjee
10.5120/8897-2920

Aman Raj, Pratik Singh, Debdutta Chatterjee . Data Mining Considering the Instances of Item-Sets. International Journal of Computer Applications. 56, 6 ( October 2012), 32-37. DOI=10.5120/8897-2920

@article{ 10.5120/8897-2920,
author = { Aman Raj, Pratik Singh, Debdutta Chatterjee },
title = { Data Mining Considering the Instances of Item-Sets },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 6 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number6/8897-2920/ },
doi = { 10.5120/8897-2920 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:10.549528+05:30
%A Aman Raj
%A Pratik Singh
%A Debdutta Chatterjee
%T Data Mining Considering the Instances of Item-Sets
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 6
%P 32-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the field of computer science, data mining is the process that attempts to discover patterns in large data sets. However it deals mostly with the relationship between two or more item objects. For example A to B, where 'A' and 'B' are the item objects. But in the real life scenario not only the relationship between item objects is important, but the relationship of their frequency of occurrence is also the matter of a prime concern. The instances of two or more data items also may be correlated with each other. For example the relation between A and 2B. Where 'A' and 'B' are the data items and '2B' represents two instances of the B type of data items. This paper provides a new approach to find the occurrence dependent data patterns by conventional approaches and also compare the some inter related concepts.

References
  1. Data mining an overview from database prospective Ming Syan Chen, Jiwaei Han, Philip . s . Yu, IEE transaction on knowledge and data engineering, Vol 8 No 6, December 1996
  2. R. Agarwal and R. Srikant, "Fast algorithms for mining Association rules in large databases", Proc. 20th Int'l Conf. Very large databases, pp. 478-499, sept 1995
  3. Data Mining: Concepts and Techniques, Second Edition Jiwai Han and Micheline Kamber, Morgan Kaufman series.
  4. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley. Chapter 6: Association Analysis: Basic Concepts and Algorithms.
  5. Rakesh Agrawal, Tomasz Imieliski, Arun Swami, Mining association rules between sets of items in large databases, ACM SIGMOD, May 1993.
  6. Feng Tao, Weighted Association Rule Mining using Weighted Support and Significant framework. ACM SIGKDD, Aug 2003
  7. K. Wang, Y. He and J. Han, Mining Frequent Itemsets Using Support Constraints, VLDB, Sep 2000
  8. Bing Liu, Wynne Hsu, Yiming Ma, Mining Association Rules with Multiple Minimum Supports. ACM SIGKDD, June 1999
  9. C. H. Cai, Ada Wai-Chee Fu, C. H. Cheng, and W. W. Kwong. Mining association rules with weighted items. IDEAS'98, July 1998.
  10. J. Han and Y. Fu, Mining Multiple-Level Association Rules in Large Databases, IEEE TKDE, September/October 1999, pp. 798-805.
  11. Jiawei Han, Jian Pei, Yiwen Yin, Mining frequent patterns without candidate generation, ACM SIGMOD, May 2000.
  12. Guimei Liu, Hongjun Lu, Yabo Xu, Jeffrey Xu Yu: Ascending Frequency Ordered Prefix-tree: Efficient Mining of Frequent Patterns. DASFAA 2003: 65-72.
  13. Jian Pei, Jiawei Han, CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets, DMKD, May 2000.
  14. Jianyong Wang, Jiawei Han, Jian Pei, CLOSET+: searching for the best strategies for mining frequent closed itemsets, ACM SIGKDD, Aug 2003.
  15. Zijian Zheng, Real World Performance of Association Rule Algorithms. ACM SIGKDD, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Data Items Instances of data items Data patterns Occurrence dependency

Powered by PhDFocusTM