research-article

An Efficient Approach to Discovering Frequent Patterns from Data Cube using Aggregation and Directed Graph

Authors:

Kuldeep Singh,

Harish Kumar Shakya,

Bhaskar BiswasAuthors Info & Claims

ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

Pages 31 - 35

https://doi.org/10.1145/2818567.2818573

Published: 25 September 2015 Publication History

Get Access

Abstract

In this paper, an algorithm has been proposed for mining frequent itemsets from data cube. Discovering frequent itemsets has been a key process in association rule mining. The major drawbacks of traditional algorithms are that lot of time consumed to find candidate itemsets and lot of memory to store them. Proposed algorithm discovers frequent itemsets using aggregation function and directed graph. It saves lot of memory consumption in candidate generation. It uses aggregation function for dimension reduction and directed graph for candidate itemsets generations. Experimental results show that the proposed algorithm can quickly discover candidate itemsets and effectively mine potential frequent patterns.

References

[1]

Agrawal, R., Srikant, R.1994. Fast Algorithms for Mining Association Rules in large databases. In proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 487--499.

Digital Library

Google Scholar

[2]

Han, J., Kamber, M. 2012. Data Mining Concepts and Techniques. In 3rd Edition, Morgan Kaufmann Publishers, San Francisco.

Digital Library

Google Scholar

[3]

Han, J., Pei, J., Yin, Y. 2000. Mining Frequent Patterns without Candidate Generation. In Proceedings of the ACM-SIGMOD conference management of data, 1--12.

Digital Library

Google Scholar

[4]

Rosen, K. H. 2012. Discrete mathematics and its application. In 7th Edition, McGraw-Hill publication.

Google Scholar

[5]

Liu, H., Wang, B. 2007. An Association Rule Mining Algorithm Based on a Boolean Matrix. In Data Science Journal, Volume 6, s559--s565.

Crossref

Google Scholar

[6]

Liu, N., Ma, L. 2013. Discovering frequent itemsets an improved algorithm of directed graph and array. In 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), 1017--1020.

Google Scholar

[7]

Fang, L. 2012. Frequent Itemset Mining Algorithm based on Graph and Two-Directional Search, In Computer engineering, Volume-38, issue-1, 59--61.

Google Scholar

[8]

Harsola, S. K., Deshpande, P. M. and Haritsa, J. R. 2012. IceCube: Efficient Targeted Mining in Data Cubes. In ICDM, 894--899.

Digital Library

Google Scholar

[9]

Xu, W. X., Wang, R. J. 2006. A Fast Algorithm of Mining Multidimensional Association Rules Frequently. In International Conference on Machine Learning and Cybernetics, China, 1199--1203.

Crossref

Google Scholar

[10]

Gray, J., Chaudhuri, S., Bosworth, A., Layman, D., Reichart, D., Venkatrao, M., Pellow, F. and Pirahesh, H. 1997. Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. In Data Mining and Knowledge Discovery archive, Vol 1, Issue 1, 29--53

Digital Library

Google Scholar

[11]

Messaoud, R. B., Rabaseda, S. L. 2006. Enhanced Mining of Association Rules from Data Cubes. In Proceedings of the 9th ACM international workshop on Data warehousing and OLAP, 11--18

Digital Library

Google Scholar

[12]

Schlimmer J., https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data

Google Scholar

[13]

Shapiro, A., https://archive.ics.uci.edu/ml/datasets/Chess (KRKPA7)

Google Scholar

Cited By

View all

Fadishei HSoltani A(2020)The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubesThe Journal of Supercomputing10.1007/s11227-019-03053-876:1(688-707)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11227-019-03053-8
Singh KBiswas B(2019)Efficient Algorithm for Mining High Utility Pattern Considering Length ConstraintsInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201907010115:3(1-27)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJDWM.2019070101
Singh KShakya HSingh ABiswas B(2018)Mining of high‐utility itemsets with negative utilityExpert Systems10.1111/exsy.1229635:6Online publication date: 5-Jul-2018
https://doi.org/10.1111/exsy.12296

Index Terms

An Efficient Approach to Discovering Frequent Patterns from Data Cube using Aggregation and Directed Graph
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining

Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based frequent pattern set becomes the first priority for a mining algorithm. ...
The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
PAKDD '09: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining

Mining frequent patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for mining frequent pattern. However, either the search space or memory space is huge, such ...
A new approach to mine frequent patterns using item-transformation methods

Mining frequent patterns is a fundamental and crucial task in data-mining problems. The algorithms reported in the literature for mining frequent patterns can be classified into two approaches: the candidate generation-and-test approach (for example, ...

Comments

Information & Contributors

Information

Published In

ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

September 2015

481 pages

ISBN:9781450335522

DOI:10.1145/2818567

© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCCT '15

ICCCT '15: Sixth International Conference on Computer and Communication Technology 2015

September 25 - 27, 2015

Allahabad, India

Acceptance Rates

Overall Acceptance Rate 33 of 124 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
78
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fadishei HSoltani A(2020)The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubesThe Journal of Supercomputing10.1007/s11227-019-03053-876:1(688-707)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11227-019-03053-8
Singh KBiswas B(2019)Efficient Algorithm for Mining High Utility Pattern Considering Length ConstraintsInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201907010115:3(1-27)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJDWM.2019070101
Singh KShakya HSingh ABiswas B(2018)Mining of high‐utility itemsets with negative utilityExpert Systems10.1111/exsy.1229635:6Online publication date: 5-Jul-2018
https://doi.org/10.1111/exsy.12296

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Discovering Periodic-Frequent Patterns in Transactional Databases

The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree

A new approach to mine frequent patterns using item-transformation methods

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations