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Hilbert Space Clustering based Chronological Backward Search for Effective Web Sequential Pattern Mining

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
A. P. Selva Prabhu, T. Ravi Chandran
10.5120/ijca2017915609

Selva A P Prabhu and Ravi T Chandran. Hilbert Space Clustering based Chronological Backward Search for Effective Web Sequential Pattern Mining. International Journal of Computer Applications 175(7):43-52, October 2017. BibTeX

@article{10.5120/ijca2017915609,
	author = {A. P. Selva Prabhu and T. Ravi Chandran},
	title = {Hilbert Space Clustering based Chronological Backward Search for Effective Web Sequential Pattern Mining},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2017},
	volume = {175},
	number = {7},
	month = {Oct},
	year = {2017},
	issn = {0975-8887},
	pages = {43-52},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume175/number7/28504-2017915609},
	doi = {10.5120/ijca2017915609},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Web data mining is an important research topic because it attains a significant amount of interest from both academic and industrial environments. Web sequential pattern mining is an imperative for analyzing the access behavior of web users. Recently, few research works have been designed for mining the web sequential patterns. However, performance of existing techniques was not effectual. In order to overcome such limitation, Hilbert Space clustering based Chronological Backward Search (HSC-CBS) Technique is proposed. HSC-CBS Technique is designed in order to improve the performance of web sequential patterns mining. The HSC-CBS Technique at first used Hilbert space clustering in order to group the similar user’s interest web patterns in web log database which resulting in improved clustering accuracy. The clustering of frequent web patterns in web log database helps for minimizing the space and time complexity of web sequential pattern mining. After clustering, HSC-CBS Technique applied chronological backward search algorithm in order to efficiently mine the web sequential patterns and improving true positive rate of web sequential pattern mining. The HSC-CBS Technique conducts the experimental works on the parameters such as execution time, space complexity, clustering accuracy, true positive rate of mining and scalability. The experimental results show that the HSC-CBS Technique is able to improve the true positive rate of pattern mining and also reduces the execution time as compared to state of the art works.

References

  1. Dawei Liu, Saifeng Cai, Xiaohong Guo, “Incremental sequential pattern mining algorithms of Web site access in grid structure database”, Neural Computing and Applications, Springer, Volume 28, Issue 3, Pages 575–583, March 2017
  2. Yiyao Lu, Hai He, Hongkun Zhao, Weiyi Meng, Clement Yu, “Annotating Search Results from Web Databases”, IEEE Transactions On Knowledge And Data Engineering, Volume 25, Issue 3, Pages 514 – 527, March 2013
  3. C. I. Ezeife, Yi Liu, “Fast incremental mining of web sequential patterns with PLWAP tree”, Data Mining and Knowledge Discovery, Springer, Volume 19, Issue 3, Pages 376–416, December 2009
  4. Jingjun Zhu, Haiyan Wu and Guozhu Gao, “An Efficient Method of Web Sequential Pattern Mining Based on Session Filter and Transaction Identification”, Journal Of Networks, Volume 5, Issue 9, Pages 1018-1024, September 2010
  5. Kuo-Wei Hsu, Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage”, Hindawi Mobile Information Systems, Volume 2017, Article ID 3689309, Pages 1-18, 2017
  6. Xiuming Yu,Meijing Li, Taewook Kim, Seon-phil Jeong and Keun Ho Ryu, “An Application of Improved Gap-BIDE Algorithm for Discovering Access Patterns”, Hindawi Publishing Corporation, Applied Computational Intelligence and Soft Computing, Volume 2012, Article ID 593147, Pages 1-7, 2012
  7. Rajashree Shettar, “Sequential Pattern Mining from Web Log Data”, International Journal of Engineering Science & Advanced Technology, Volume 2, Issue 2, Pages 204 – 208, 2012
  8. Jaymin Desai, Risha Tiwari, “Web Log Mining Using Multiitem Sequntial Pattern Based On PLWAP”, International Journal For Technological Research In Engineering, Volume 4, Issue 7, Pages 1015-1016, March-2017
  9. Hemraj K. Varachhia, Ankur N. Shah, “A Systematic Review on Mining Web Navigation Pattern Using Graph Based Techniques”, International Journal of Scientific & Technology Research Volume 3, Issue 7, Pages 76-79, July 2014
  10. Zhengyu Zhu, Meiyu Zheng, Yihan Wu, “A Web Log Frequent Sequential Pattern Mining Algorithm Linked WAP-Tree”, Journal of Software, Volume 10, Number 10, Pages 1228-1234, October 2015
  11. K. Suneetha, M. Usha Rani, “Finding of Weighted Sequential Web Access Patterns for Effective Web Page Recommendations”, International Journal of Computer Science and Technology, Volume 3, Issue 3, Pages 884-888, 2012
  12. Xiuming Yu, Meijing Li, Kyung Ah Kim, Jimoon Chung and Keun Ho Ryu, “Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph”, Sustainability, Volume 8, Pages 1-18, 2016
  13. Meijing Li, Xiuming Yu, Keun Ho Ryu, “MapReduce-based web mining for prediction of web-user navigation”, Journal of Information Science, Volume 40, Issue 5, Pages 557-567, 2014
  14. Kuldeep Singh Rathore, Sanjiv Sharma, “Web Personalization Based on Enhanced Web Access Pattern using Sequential Pattern Mining”, International Journal Of Engineering And Computer Science, Volume 5, Issues 7, Pages 17152-17159, 2016
  15. A. Anitha, “An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification”, Circuits and Systems, Volume 7, Pages 2349-2356, 2016
  16. Oznur Kirmemis Alkan, Pinar Karagoz, “WaPUPS:Web access pattern extraction under user-defined pattern scoring”, Journal of Information Science, Volume 42, Issue 2, Pages 261 – 273, 2015
  17. Binu Thomas and G. Raju, “A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules”, Hindawi Publishing Corporation, ISRN Artificial Intelligence, Volume 2013 (2013), Article ID 316913, Pages 1-10
  18. Amina Kemmar, Yahia Lebbah, Samir Loudni, “A Constraint Programming Approach for Web Log Mining”, International Journal of Information Technology and Web Engineering, Volume 11 Issue 4, Pages 24-42,October 2016
  19. Nu Yin Kyaw, “Creating User Interesting Usage Access Pattern using Statistical Data”, International Journal of Scientific Engineering and Technology Research, Volume 03, Issue 18, Pages: 3695-3700, August-2014
  20. Sheng-Tang Wu and Yuefeng Li, “Pattern-Based Web Mining Using Data Mining Techniques”, International Journal of e-Education, e-Business, e-Management and e-Learning, Volume 3, Issue 2, Pages 163-167, April 2013
  21. Amazon Commerce reviews set Data Set: https://archive.ics.uci.edu/ml/datasets/Amazon+Commerce+reviews+set

Keywords

Chronological backward search, Hilbert Space clustering, mining, web user, web log database, web pattern.