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Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review

by Alisha S. Patel, Mohit Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 8
Year of Publication: 2015
Authors: Alisha S. Patel, Mohit Patel
10.5120/19212-1046

Alisha S. Patel, Mohit Patel . Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review. International Journal of Computer Applications. 109, 8 ( January 2015), 46-49. DOI=10.5120/19212-1046

@article{ 10.5120/19212-1046,
author = { Alisha S. Patel, Mohit Patel },
title = { Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 8 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 46-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number8/19212-1046/ },
doi = { 10.5120/19212-1046 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:44:18.127409+05:30
%A Alisha S. Patel
%A Mohit Patel
%T Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 8
%P 46-49
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods. MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.

References
  1. Huang QingLan, Duan LongZhen, "Multi-level association rule mining based on clustering partition", IEEE(2013).
  2. Pratima Gautam, Dr. K. R. Pardasani , "Algorithm for Efficient Multilevel Association Rule Mining", IJCSE(2010).
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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Association Rule Mining Multilevel Association Rule Mining Concept Hierarchy.

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