CFP last date
20 December 2024
Reseach Article

Development of Decision Tree Algorithm for Mining Web Data Stream

by Sheetal Sharma, Swati Singh Lodhi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 2
Year of Publication: 2016
Authors: Sheetal Sharma, Swati Singh Lodhi
10.5120/ijca2016908770

Sheetal Sharma, Swati Singh Lodhi . Development of Decision Tree Algorithm for Mining Web Data Stream. International Journal of Computer Applications. 138, 2 ( March 2016), 34-43. DOI=10.5120/ijca2016908770

@article{ 10.5120/ijca2016908770,
author = { Sheetal Sharma, Swati Singh Lodhi },
title = { Development of Decision Tree Algorithm for Mining Web Data Stream },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 2 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number2/24354-2016908770/ },
doi = { 10.5120/ijca2016908770 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:38.238450+05:30
%A Sheetal Sharma
%A Swati Singh Lodhi
%T Development of Decision Tree Algorithm for Mining Web Data Stream
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 2
%P 34-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

World Wide Web presents challenging aspects or task for mining web data stream. Currently processing of useful data from web data stream is getting complex because when we considering the large volume of web log data it does not provide well-structured data. Two major challenge involved in web usage mining are processing the raw data to provide a (very close to the truth or true number) picture of how site is being used, and filtering the result of different data mining set of computer instructions in order to present only rules and patterns. In this work we develop decision tree algorithm, which is efficient mining method to mine log files and extract knowledge from web data stream and generated training rules and Pattern which are helpful to find out different information related to log file.

References
  1. Nazli Mohd Khairudin, AidaMustapha, andMohd Hanif Ahmad (2014). Effect of Temporal Relationships in Associative Rule Mining for Web Log Data System. Hindawi Publishing Corporation Scientific World Journal.
  2. Agrawal, M. Husain, R. G. Tiwari, and S. Vishwakarma(2011), “Web information recuperation from strewn text resource systems,”International Journal of Advances in Engineering and Technology,vol. 1, no. 2, pp. 126–137
  3. Arumugam G. and Suguna S(2009),“Optimal Algorithms for Generation of User Session Sequences Using Server Side Web User Logs, “,ESRGroups, France
  4. D. Vasumathi and A. Govardhan(2009), “Efficient web usage mining based on formal concept analysis,” Journal of Theoretical and Applied Information Technology, vol. 9, no. 2, pp. 99–109.
  5. Archana N.Mahanta(2008) ,“Web Mining:Application of Data Mining,”,of NCKM ,
  6. Chungsheng Zhang and Liyan Zhuang(2008) , “New Path Filling Method on Data Preprocessing in Web Mining ,“, Computer and Information Science Journal
  7. V. S. Tseng, K.W. Lin, and J.-C. Chang(2008), “Prediction of user navigation patterns by mining the temporal web usage evolution,” Soft Computing, vol. 12, no. 2, pp. 157–163
  8. Zhuang, L., Kou, Z., & Zhang, C. (2005). Session identification based on time interval in web log mining. In Intelligent information processing II (pp. 389-396): Springer-Verlag
  9. E. Winarko and J. F. Roddick(2007), “ARMADA—an algorithm for discovering richer relative temporal association rules from interval-based data,” Data and Knowledge Engineering, vol. 63, no. 1, pp. 76–90 .
  10. E. Keogh, J. Lin, S.-H. Lee(2007), and H. Van Herle, “Finding the most unusual time series subsequence: algorithms and applications,” Knowledge and Information Systems, vol. 11, no. 1, pp. 1–27,
  11. Jose M. Domenech1 and Javier Lorenzo(2007), “A Tool for Web Usage Mining , “ , 8th International Conference on Intelligent Data Engineering and Automated Learning.
  12. Jungie Chen and Wei Liu(2006), “Research for Web Usage Mining Model,”, International Conference on Computational Intelligence for Modelling Control and Automation, IEEE.
  13. Suresh R.M. and Padmajavalli .R.(2006) ,“An Overview of Data Preprocessing in Data and Web usage Mining ,“ IEEE.
  14. K. Verma and O. P. Vyas(2005), “Efficient calendar based temporal association rule,” SIGMOD Record, vol. 34, no. 3, pp. 63–70.
  15. Y. Li, P.Ning, X. S.Wang(2003), and S. Jajodia, “Discovering calendarbased temporal association rules,” Data and Knowledge Engineering, vol. 44, no. 2, pp. 193–218.
  16. Gaul, W., & Schmidt-Thieme, L. (2001). Mining Generalized Association Rules for Sequential and Path Data. Proceedings of the 2001 IEEE International Conference on Data Mining.
  17. Wang, S., Gao, W., & Li, J. (2000). Discovering Sequence Association Rules with User Access Transaction Grammars. Proceedings of the 11th International Workshop on Database and Expert Systems Applications.
  18. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.-N. (2000). Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explorations Newsletter, 1(2), 12-23
  19. J. M. Ale and G. H. Rossi(2000), “An approach to discovering temporal association rules,” in Proceedings of the ACM Symposium on Applied Computing (SAC ’00), vol. 1, pp. 294–300.
  20. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. SIGMOD Rec., 29(2), 1-12.
  21. Cooley, R. (1997). Web Mining: Information and Pattern Discovery on the World Wide Web. Proceedings of the 9th International Conference on Tools with Artificial Intelligence.
  22. Agrawal, R., & Srikant, R. (1995). Mining sequentialpatterns. Proceedings of the Eleventh International Conference on Data Engineering, 1995
Index Terms

Computer Science
Information Sciences

Keywords

Web Usage Mining Decision Tree Temporal Rule Mining...