Web Personalization with Usage-Based Clustering

Author:

Mrs. M. M. Mali 1,Mrs. S. L. Mortale 1,Mrs. M. A. Parlikar 1,Mrs. T. H. Gavhane 1,Mrs. A. S. Sawalkar 1

Affiliation:

1. Pimpri Chinchwad Polytechnic, Pune, Maharashtra, India

Abstract

Information on World Wide Web has been filling in a remarkable way. This raises a serious worry on data over-burden difficulties for the clients. Recovering the most significant data from the web according to the client prerequisite has become hard on account of the enormous assortment of heterogeneous archives. One way to deal with beat this is to customize the data accessible on the Web as indicated by client necessities. This is called Web Personalization process that changes data/administrations conveyed by a Web to the necessities of every client or gathering of clients, taking their standards of conduct. Successive Sequential Patterns (FSPs) that are separated from Web Usage Data (WUD) are vital for dissecting and understanding clients' way of behaving to work on the nature of administrations presented by the World Wide Web (WWW). Client standards of conduct are expected to fabricate profiles of every client, it is made to utilize which Personalization of site.

Publisher

Naksh Solutions

Subject

General Medicine

Reference6 articles.

1. Madhavi M.Mali,Sonal S.Jogdand, Deepali P. Shinde, “Web Personalization Using Usage Based Clustering”, International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online Journal) Volume1, Issue3, Nov-Dec, 2014.ISSN: 2349-7173(Online).

2. Madhavi M.Mali,Sonal S.Jogdand, Deepali P. Shinde, “Personalized Look and Feel Through Web Usage Mining”, International Journal of Current Research Vol. 7, Issue, 02, pp.12396-12399, February, 2015.

3. Kartik Menon and Cihan H. Dagli, “Web Personalization using Neuro-Fuzzy Clustering Algorithms”, Smart Engineering Systems Laboratory University of Missouri – Rolla, 2003 IEEE.

4. D.Vasumathi, A.Govardhan, K.Suresh, “Effective Web Personalization Using Clustering”, 2009 IEEE.

5. BamshadMobasher, Robert Cooley, Jaideep Srivastava, “Creating Adaptive Web Sites Through Usage-Based Clustering of URLs”, IEEE.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3