Cluster Based Web Search Using Support Vector Machine

Now days, searches for the web pages of a person with a given name constitute a notablefraction of queries to Web search engines. This method exploits a variety of semantic informationextracted from web pages.The rapid growth of the Internet has made the Web a popular place for collecting information.Today, Internet user access billions of web pages online using search engines. Information in theWeb comes from many sources, including websites of companies, organizations,communications and personal homepages, etc. Effective representation of Web search resultsremains an open problem in the Information Retrieval community. For ambiguous queries, atraditional approach is to organize search results into groups (clusters), one for each meaning ofthe query. These groups are usually constructed according to the topical similarity of the retrieveddocuments, but it is possible for documents to be totally dissimilar and still correspond to thesame meaning of the query. To overcome this problem, the relevant Web pages are often locatedclose to each other in the Web graph of hyperlinks. It presents a graphical approach for entityresolution & complements the traditional methodology with the analysis of the entity-relationship(ER) graph constructed for the dataset being analyzed. It also demonstrates a technique thatmeasures the degree of interconnectedness between various pairs of nodes in the graph. It cansignificantly improve the quality of entity resolution.Using Support Vector Machines (SVMs) which are a set of related Supervised learning methodsused for classification of load of user queries to the sever machine to different client machines sothat system will be stable. Cluster web pages based on their capacities stores whole database onserver machine.