Efficient data cleaning algorithm using decision tree classification model approach and modified new unique user identification algorithm using hashing techniques with a new error factor

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The study focuses on preprocessing techniques of web mining. Considering this scope, the study has proposed and implemented an efficient data cleaning and unique user identification algorithms. Previously proposed data cleaning algorithm is a generalized approach and lacked transparency. An appropriate model has to be used to implement the new data cleaning algorithm. Over analysis of various related studies and suggestions made by eminent experts, the study finalized decision tree classification model, and appropriate model to implement the new data cleaning algorithm. Simplicity, ease in framing rules and ability to fragment complex decisions to solve a problem motivated to choose decision tree classification model to implement new data cleaning algorithm. Apart from this the study has also modified the previously proposed hash function, used to locate existing web users in web log server. A new error factor is introduced to remove memory address discrepancy. The modified hashing function along with binary search techniques is used to design the new unique user identification algorithm. Various experiments analysis is done using web log servers of eminent universities and colleges from United Arab Emirates and India. Results obtained prove the improved and better performances of the new rule based data cleaning and modified unique user identification algorithms.

  • Keywords

    Use about five key words or phrases in alphabetical order, Separated by Semicolon.

  • References

      [1] Chitraa V &Dr.AntonySelvadoss, “A Novel Technique for Sessions Identification in Web Usage Mining Preprocessing”,International Journal of Computer Applications, Vol.34,No.9, (2011), pp.23-31.

      [2] Suguna R & Sharmila D, “User Interest Level Based Pre-processing Algorithms Using Web Usage Mining”, International Journal on Computer Science and Engineering, Vol.10, (2015), pp.108-117.

      [3] Vaarandi R &Pihelgas M, “Logcluster-a data clustering and pattern mining algorithm for event logs”, 11th International Conference on Network and Service Management, (2015), pp.1-7.https://doi.org/10.1109/CNSM.2015.7367331.

      [4] Jagan S & Rajagopalan SP, “A Survey on Web Personalization of Web Usage Mining”, International Research Journal of Engineering and Technology, Vol.02, No.01, (2015), pp.2395-0056.

      [5] Parmar VP &Kumbharana CK, “Comparing Linear Search and Binary Search Algorithms to Search an Element from a Linear List Implemented through Static Array, Dynamic Array and Linked List”, International Journal of Computer Applications, Vol.121, No.3, (2015).

      [6] Ranjena Sriram & Mallika R, “Innovative Pre-Processing Technique and Efficient User Identification Algorithm for Web Usage Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.6, No.2, (2016), pp.85-90.

      [7] Sleator DD &Tarjan RE, “Self-adjusting binary search trees”, Journal of the ACM (JACM), Vol.32, No.3, (1985), pp.652-686.https://doi.org/10.1145/3828.3835.

      [8] Singh K &Sulekh R., “The Comparison of Various Decision Tree Algorithms for Data Analysis”, International Journal of Engineering and Computer Science, Vol.6, No.6, (2017).

      [9] Sewaiwar P & Verma KK, “Comparative Study of Various Decision Tree Classification Algorithm Using WEKA”, International Journal of Emerging Research in Management &Technology, Vol.4, (2015), pp.2278-9359.

      [10] Chourasia S, “Survey paper on improved methods of ID3 decision tree classification”, International Journal of Scientific and Research Publications, Vol.3, No.12, (2013).

      [11] Vadhera P &Lall B, “Review Paper on Secure Hashing Algorithm and Its Variants”, International Journal of Science and Research (IJSR), Vol.3, No.6, (2012), pp.55-61.

      [12] Raiyani SA, “Preprocessing and Analysis of Web Server Logs”, International Journal of Computer Science & Communication Networks, Vol.2, (2015), pp.46-55.

      [13] Suneetha KR &Krishnamoorthi R, “Identifying User Behavior by Analyzing Web Server Access Log File”, International Journal of Computer Science and Network Security, Vol.9, No.4, (2009), pp.327-332.

      [14] Sahu MS &Sahu APL, “A Survey on Frequent Web Page Mining with Improving Data Quality of Log Cleaner”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol.4, No.3, (2015), pp.825-829.




Article ID: 9736
DOI: 10.14419/ijet.v7i1.9.9736

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.