Application of webpage optimization for clustering system on search engine v google study

Abstract

As of March 2012, there are over six hundred and forty million active websites [5], amid such immense data in the ocean of network, user`s browsing behavior to retrieve information will affect the level of webpage exposure directly. Benjamin Edelman, Michael Ostrovsky and Michael Schwarz reckoned that once obtained a more advanced ranking on search engine, one can obtain higher click through rate [1]. The objective of this study- `Application of webpage optimization for clustering system on search engine- Google study` is to utilize the technologies of TF-IDF, K-means clustering and indexing quality examination to identify the combination of key words that will benefit search engine optimization. The study demonstrated that it can effectively enhance the website`s advancement of ranking on search engine, increase website`s exposure level and click through rate. © 2014 IEEE.