CFP last date
20 May 2024
Reseach Article

Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation

by G. Gunasekaran, S. Murugan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 7
Year of Publication: 2017
Authors: G. Gunasekaran, S. Murugan
10.5120/ijca2017915973

G. Gunasekaran, S. Murugan . Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation. International Journal of Computer Applications. 179, 7 ( Dec 2017), 32-40. DOI=10.5120/ijca2017915973

@article{ 10.5120/ijca2017915973,
author = { G. Gunasekaran, S. Murugan },
title = { Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 179 },
number = { 7 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 32-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number7/28750-2017915973/ },
doi = { 10.5120/ijca2017915973 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:54:44.021494+05:30
%A G. Gunasekaran
%A S. Murugan
%T Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 7
%P 32-40
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Advancements in information technology increase the data volume of many domains into manyfold. Dynamic Memory Efficient Frequent Pattern (DMEFP) technique introduces new methods to represent data and redundant frequent patterns. Introduction of Repeat Pattern Table (RPT) and new node type ‘Tree Pattern Node’ (TPN) in frequent pattern tree softens the data mining process to be performed in a modern way. DMEFP technique comprises new rules to aggregate pattern nodes and RPT. Computational resources are used sagely in DMEFP technique for data mining. Reduced resource consumption helps to parse large amount of data in short time durations without much complexity.

References
  1. Chien Chiang Lin, Hsing-Hung Lin, Kun-Chih Huang. TRIZ retrospect and prospect. Systems and Informatics (ICSAI) IEEE November 2016
  2. Jeff Heaton. Comparing dataset characteristics that favor the Apriori, Eclat or FP-Growth frequent itemset mining algorithms. IEEE April 2016
  3. Arkan A.G.AL-Hamodi, Songfeng LU, Yahya E.A.AL-Salhi. AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAPREDUCE FOR MINING ASSOCIATION RULES. International Journal of Data Mining & Knowledge Management Process (IJDKP) March 2016
  4. Dea Delvia Arifin, Shaufiah, Moch.Arif Bijaksana. Enhancing Spam Detection on Mobile Phone Short Message Service (SMS) Performance using FP-Growth and Naive Bayes Classifier. Asia Pacific Conference on Wireless and Mobile (APWiMob). IEEE 2016
  5. Chunkai Zhang, Xudong Zhang, Panbo Tian. An Approximate Approach to Frequent Itemset Mining, Data Science in Cyberspace (DSC). IEEE June 2017
  6. Wan Aezwani Bt Wan Abu Bakar, Zailani B.Abdullah, Md.Yazid B.Md Saman, Masila Bt Abd Jalil, Mustafa B. Man, Tutut Herawan, Abdul Razak Hamdan. Incremental-Eclat Model: An Implementation via Benchmark Case Study. Advances in Machine Learning and Signal Processing. Springer June 2016
  7. Iona Sudheendran, Ganesh Kumar R. A Dynamic Approach for Frequent Pattern Mining Using Database Characteristics. International Journal for Research in Applied Science & Engineering Technology (IJRASET) MAY 2015
  8. Sagar Bhise1, Prof. Sweta Kale, Effieient Algorithms to find Frequent Itemset Using Data Mining, International Research Journal of Engineering and Technology (IRJET) JUNE 2017
  9. Duo Liu, Yi Lin, Po-Chun Huang, Xiao Zhu, Liang Liang. Durable and Energy Efficient In-Memory Frequent Pattern Mining. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE March 2017
  10. Mukesh Bathre, Vivek Kumar Vaidya, Alok Sahelay. Memory Efficient Frequent Pattern Mining using Transposition of Database. International Journal of Computer Engineering & Technology (IJCET). APRIL 2016
  11. Data Mining Algorithms In R/Frequent Pattern Mining/The FPGrowthAlgorithm,https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm
  12. Mahito Sugiyama, Karsten M. Borgwardt, Significant Pattern Mining on Continuous Variables. Cornell University Library 2017
  13. Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal. Data Mining: Practical machine learning tools and techniques. Fourth Edition
  14. Mahsa Salehi, Christopher Leckie, James C.Bezdek, Tharshan Vaithianathan, Xuyun Zhang. Fast Memory Efficient Local Outlier Detection in Data Streams, IEEE Transactions on Knowledge and Data Engineering, IEEE December 2016
  15. Souleymane Zida, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng. EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowledge and Information Systems. Springer May,2017
Index Terms

Computer Science
Information Sciences

Keywords

Information Technology Data Mining FP-Growth Frequent Pattern Tree Memory efficient data mining