This paper performs a detailed discussion about the clustering techniques being proposed by different researchers on Webinars in such a way to support E-Learning Methods. The growth of information technology has lead the learning methods towards webinar where the learner need not go to the place of the tutor or the institution. In providing efficient learning methods and to provide exact information to the learner, clustering the webinar’s become more important. To solve the problem of clustering webinar’s there are different approaches has been discussed from content based, context based, text based, Speech analysis based, and Ontology based. Each of the approach has their own merits and demerits. Also in the clustering, there are approaches like supervised and unsupervised and hierarchical clustering has been discussed. We explore each of them in detail and perform the analysis of each technique according to their performance in supporting E-Learning. The paper also discusses about different data miningtechniques could be adapted for E-Learning in detail.
.Nikos Manouselis, George Kyrgiazos, Giannis Stoitsis, Exploratory study of multi-criteria recommendation algorithms over technology enhanced learning datasets, Journal of e-Learning and Knowledge Society, Vol.10, No.1, 2014.  Rashmi Rekha Borah,” Slow Learners: Role of Teachers and Guardians in Honing their Hidden Skills”, Int. J. of Educational Planning & Administration, vol. 3, no. 2, pp. 139-143, 2013.  Clark, R, C. & Mayer, R.M.,” E-Learning and the science of instruction”, 3rd edition, San Francisco, CA: Pfeiffer. 2011.  J.willems, “Using Learning styles data to inform e-learning design: A study comparing undergraduates, postgraduates and e-educators”, Australasian J.of. Educational technology, vol.27, no.6, pp.863-880, 2011.  Movafegh Ghadirli, H. and Rastgarpour, M.,, “A Model for an Intelligent and Adaptive Tutor based on Web by Jackson’s Learning Styles Profiler and Expert Systems”, Proc.of the Int. MultiConferece of Engineers and Computer Scientists(IMECS 2012) ,vol 1, 2012.  Al-Khalifa, H. S. and Al-Wabel, A. S. 2005. Aided technological methods for special learning: exploratory study. Available online: http://www.gulfkids.com/pdf/Areej.pdf, 2013.  Anitha, A and Krishnan. “A Dynamic Web Mining Framework for E-Learning Recommendations using Rough Sets and Association Rule Mining’. International Journal of Computer Applications, vol.12, no.11, pp.36-41, 2011.  Kumar, V. and Chadha, A. “An Empirical Study of the Applications of Data Mining Techniques in Higher Education”, Nit’s. Of Advanced Computer Science and Applications, vol. 2, no. 3, pp. 80-84, 2011.  Kangaiammal, A., Silambannan, R., Senthamarai, C., and Srinath, M.,” Student Learning Ability Assessment using Rough Set and Data Mining Approaches’. I.J.Modern Education and Computer Science, vol. 5, pp.1-11, 2013.