CFP last date
20 January 2025
Reseach Article

A Survey Paper on: Frequent Pattern Analysis Algorithm from the Web Log Data

by Samiksha Kankane, Vikram Garg
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 13
Year of Publication: 2015
Authors: Samiksha Kankane, Vikram Garg
10.5120/21129-3904

Samiksha Kankane, Vikram Garg . A Survey Paper on: Frequent Pattern Analysis Algorithm from the Web Log Data. International Journal of Computer Applications. 119, 13 ( June 2015), 27-29. DOI=10.5120/21129-3904

@article{ 10.5120/21129-3904,
author = { Samiksha Kankane, Vikram Garg },
title = { A Survey Paper on: Frequent Pattern Analysis Algorithm from the Web Log Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 13 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 27-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number13/21129-3904/ },
doi = { 10.5120/21129-3904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:57.381546+05:30
%A Samiksha Kankane
%A Vikram Garg
%T A Survey Paper on: Frequent Pattern Analysis Algorithm from the Web Log Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 13
%P 27-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web data mining is an emerging research area where mining data is an important task and various algorithms has been proposed in order to solve the various issues related to the web mining in existing dataset. This paper focuses the concept of data mining and FP-Growth algorithm. As for FP-Growth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. This Research work concentrates on web usage mining and in particular focuses on discovering the web usage patterns of web sites from the server log files. This paper finds the procedure to work with the proposed technique which can be possible to remove the drawback of limitation of the existed technique in the web mining area. The various web usages mining technique can further work on various scientific area, medical area and social media application to approach for the research and security related area. A detail and pattern growth technique can help in getting more data and further on using line up algorithm we can illustrate the data states presentation effectively.

References
  1. Hao Yan, Bo Zhang, Yibo Zhang, Fang Liu, Zhenming Lei "Web usage mining based on WAN users behaviours" 2010.
  2. Huiping Peng "Discovery of Interesting Association Rules Based on Web Usage Mining" 2010.
  3. Iqbal Gondal and Joarder Kamruzzaman Md. Mamunur Rashid, "Mining Associated Sensor Pattern for data stream oorks"Spain, 2013.
  4. Joy Shalom Sona, AshaAmbhaikar "Reconciling the Website Structure to Improve the Web Navigation Efficiency" July 2012.
  5. K. R. Suneetha, Dr. R. Krishnamoorthi, "Identifying User Behaviour by Analyzing Web Server Access log"2009.
  6. Luca Cagliero and Paolo Garza "Infrequent Weighted Itemset Mining using Frequent Pattern Growth", IEEE Transactions on Knowledge and Data Engineering, 2013.
  7. Min Chen and young U. Ryu "Facilitating Effective User Navigation through Website Structure Improvement" IEEE KDD, 2011.
  8. Sanjay Kumar Malik, Nupur Prakash, SamRizvi" Ontology and Web Usage Mining towards an Intelligent Web focusing web logs" 2010.
  9. Xiaoting Wei, Yunlong, Feng Zhang, Min Liu, WeimingShen Incremental FP-Growth Mining Strategy for Dynamic Threshold Value and Database Based on MapReduce School of Electronic and Information Engineering, Tongji University, Shanghai ,China IEEE2014.
  10. Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister and Marc Streit, "LineUp: Visual Analysis of Multi-Attribute Rankings" IEEE 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Ranking Clustering Web Logs