The week's pick
Reseach Article
Analysis of Web Performance based on Navigation Pattern using Progressive Web Datasets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 148 - Number 4 |
| Year of Publication: 2016 |
| Authors: Sapana Kumari, Vikram Garg |
10.5120/ijca2016911091
|
Sapana Kumari, Vikram Garg . Analysis of Web Performance based on Navigation Pattern using Progressive Web Datasets. International Journal of Computer Applications. 148, 4 ( Aug 2016), 34-36. DOI=10.5120/ijca2016911091
Abstract
Now a day’s the e-commerce websites are facing the biggest challenges of the massive growth of website data. There is a need to find behavior of user so that there is need to find next page in advance on cache. Further they do not have a good policy for finding user behavior in website. They need to use good approach for improving the quality and accuracy in today’s scenario. Closed sequential pattern mining is an important technique among the different types of sequential pattern mining, since it preserves the details of the full pattern set and it is more compact than sequential pattern mining. In this paper the clustering task is used to improve performance of website navigation pattern in advance. The main goal of this research is to find the extract the knowledge that can enhance web performance of associate items in sequential manner with the quality.
References
- Md. Hedayetul Islam Shovon, Mahfuza Haque “Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering , Volume 2, Issue 7, ISSN: 2277 128X, pp 353-355, July 2012,
- Kavita Nagar “Data Mining Clustering Methods: A Review” International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Volume 5, Issue 4, ISS: 2277 128X, pp 575-579, 2015, www.ijarcsse.com.
- Oyelade, O. J, Oladipupo, O. O, Obagbuwa, I. C “Application of k-Means Clustering Algorithm for Prediction of Student’s Academic Performance”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 1, 2010, pp 292-295.
- K. C. Kandpal and R. Agnihotril, Sblock – A closed sequential pattern mining algorithm, International Journal of Computer Applications in Engineering Sciences, vol.1, no.3, pp.296-299, 2011.
- S.-Y. Yang, C.-M. Chao, P.-Z. Chen and C.-H. Sun, Incremental mining of across-streams sequential patterns in multiple data streams, Journal of Computers, vol.6, no.3, pp.449-457, 2011.
- S.-Y. Yang, C.-M. Chao, P.-Z. Chen and C.-H. Sun, Incremental mining of closed sequential patterns in multiple data streams, Journal of Networks, vol.6, no.5, pp.728-735, 2011.
- G. Lee, K.-C. Hung and Y.-C. Chen, Path tree: Mining sequential patterns efficiently in data streams environments, Intelligent Systems and Applications, SIST, vol.20, pp.261-268, 2013.
- T. Guyet and R. Quiniou, Incremental mining of frequent sequences from a window sliding over a stream of itemsets, Actes IAF, 2012.
- A. Koper and H. S. Nguyen, Sequential pattern mining from stream data, Advanced Data Mining and Applications, vol.7121, pp.278-291, 2011.
- L. Vinceslas, J.-E. Symphor, A. Mancheron and P. Poncelet, SPAMS: A novel incremental approach for sequential pattern mining in data streams, Advances in Knowledge Discovery and Management, vol.292, pp.201-216, 2010.
- Miss. Vrinda Khairnar and Miss. Sonal Patil, "Efficient clustering of data using improved K-means algorithm", Imperial Journal of Interdisciplinary Research (IJIR), Vol.2, Issue-1, 2016.
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