The dangers phishing becomes considerably bigger problem in online networking, for example, Facebook, twitter and Google+. The phishing is normally completed by email mocking or texting and it frequently guides client to enter points of interest at a phony sites whose look and feel are practically indistinguishable to the honest to goodness. Non-technical user resists learning of anti-phishing tech-nic. Also not permanently remember phishing learning. Software solutions such as authentication and security warnings are still depending on end user action. In this paper we are mainly focus on a novel approach of real time phishing email classification using K-means algorithm. For this we uses 160 emails of last year computer engineering students. we get True positive of legitimate and phishing as 67% and 80% and true negative is 30 % and 20%.,which is very high so we ask same users reasons which I mainly categories into three categories ,look and feel of email, email technical parameters, and email structure.