WWW is one of the popular resource for text, image, audio, video, and metadata .In order to analyze such data, some technique called web mining technique are used by various web application and tools. Web mining describes the use of data mining technique to automatically discover Web documents and services, to extract information from the web resource and to uncover the general pattern on the Web. However, with the overwhelming volume of information on the Web, the task of finding relevant information related to a specific query /topic is becoming increasingly difficult. Many advanced Web searching technique have been recently developed to taken this problem and are being used in the commercial Web search engines such as Google and Yahoo. Google [3] has been found out that more than 50% of the search engine users consult no more than first two screens of results [4]. To get the required information, the user may have to sift through a very large list of documents displayed by search engines, posing the problem of information overkill thus necessitating the need to look for alternative techniques for documents presentation. In search engines [2] .Instead, the problem is that a search engine returns a large number of web pages in response to user queries and users have to spend much time in finding their desired information from the long list resulting in information overload problem [2] .Almost all search engines store their user activities in the form of query logs . Query logs provide an excellent opportunity for gaining insight into how a search engine is used and what the user’s interest are since they form a complete record of what use searched for in a given time frame.

RELATED WORK

The notation of Web log Mining has been a subject of interest since many years. Most of the search engines use Page ranking algorithms, which can arrange the documents in order of their relevance, importance and content Page ranking algorithms [9, 10] have been proposed in the literature such as Page Rank, Weighted Page Rank. The typical logs [3] of search engine includes the following entries (1) User IDs , (2) Query q issued by the user, (3) URL u selected by the user, (4) Rank r of the URL u clicked for the query q and (5) Time t at which the query has been submitted for search.Table1 for this format. A number of researchers analyzing the problems of query logs [6, 7, 8, 9].The information contained in query logs has been used in many different ways, for examine to provide context doing search to clustering values. In values studies [9, 10], researcher and search engine operators have used information from

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Page Rank [10, 11, 12] was developed at Stanford University by Larry page (cofounder of Google search engine) and Sergey Brin. Google uses this algorithm to order its search results in such a way that important documents move up in the results of a search while moving the less important pages down in its list. This algorithm states that if a page has some important incoming links to it, then its outgoing links to other pages also become important, thus it takes back links into account and propagates the ranking through links. When some query is given, Google combines precompiled Page Rank scores with text matching scores to obtain an overall ranking score for each resulted web page in response to the query. Although many factors determine the ranking of Google search results but Page Rank continues to provide the basis for all of Google's web search tools. Random Surfer Model [11] which states that not all users follow the direct links on WWW. The modified version is given in (1). PR (u) = (1-d) + d Σ Pr (v) V€B(u) Vn Figure: Priority Based Search Engine Architecture When a user submit a query on the search engine interface, the query processor component match the query terms with the index repository of the search engine and return a list of matched documents in response .The user browsing behavior including the submitted queries and clicked URLs get stored in the logs .The Rank Updater component works online and takes input the matched documents received by query processor. It improves the ranks of page based on the weights assigned to each according to a sequential pattern which were discovered offline. And a heap sort The working and algorithm for different functional modules are explained below. a) WWW: The World Wide Web is a system of interlinked hypertext documents accessed via the Internet With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks b) Crawler: A Web crawler is one type of boot, or software agent, or computer program, it starts with a list of URLs to visit, it identifies all the hyperlinks in the page and adds them to the list of URLs to visit, called the crawl frontier. Web crawling providing up-to-date data. Web crawlers are mainly used to create a copy of all the visited pages for later processing by a search engine that will index the downloaded pages to provide fast searches. Crawlers can also be used for automating maintenance tasks on a Web site, such as checking links or validating HTML code. c) Search Interface: It is a Graphical interface of a search engine on which the user can enter his query e.g. the Google interface. There are two parts to a search engine's user experience: the user interface (design of the search forms and results pages) and the functionality (how well it matches and sorts pages). When you install a search engine, you should consider both aspects, design the interface carefully and test all aspects of the usability. d) Query Processor: It is the component used to taking the user query from the search interface and processing it word by word. e) Indexer: It collects and stores data to facilitate fast and accurate information retrieval. The index is usually built in an alphabetical order of term and contains extra information regarding the page such as its URL, frequency and position of term. It provides a more useful vocabulary for the internet or onsite search engine. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, physics, and computer science. An alternate name for the process in the context of search engines designed to find web pages on the Internet is web indexing. f) Map Generator: This module generates a map/graphical structure of the WWW. The

g) Rank improvement: The rank of a page can be improved with the help of its assigned weight .The new rank now becomes: New Rank(X) =Rank(X) +Weight(X) The algorithm is based on the simple perspective; initially all queries are considered to be unassigned to any cluster. Each unclassified query is examined against all other queries. Algorithm: rank improve (Q,n) Given: A set of n queries and corresponding clicked URLs stored array Q [qi, URL1……., URLm], 1≤i≤n Output: A set C= {C1, C2…, Ck} of k query. // Start of algorithm {

K=0;

For (each query P in Q) Set Clusterid (P) = NULL; For (each P€ Q with clustered (P) = NULL) { I = n, page = Q (n); Clusterid (p) =ck; Weight(X) = In (lenpar(X)) level(X) Page_ rank(X) = (1-d) +d Σ PR (v) V€B(X) Nv NewPage _rank(X) = Page_ rank + Weight(X) For (j=2; j≤page; j++) { NewPage_rank= Q[j]; i=j-1; While ((i≥1) && (item

Double alpha=0.85; Double beeta=0.05; Double gaama=0.10; Double damping factor=0.84;

In this formula,

• sr-> previous rank of that page

• bl->in links/ back links

• Nod->number of duplicate

 Out links->going to other pages

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fromUsers’past behavior. In this paper the Page Rank and Weighted Page Rank algorithms are used by many search engine but the users may not get the required relevant documents easily on the top few pages. To solve this problem we use the Weighted Page Content Rank has been proposed which employ Web structure mining as well as Web Content mining technique. This algo is improving the order of the page in the result list so that the user gets the relevant and important pages in the list. paragraphs/sections . A query log analysis the proposed for implementing effective web search. The most important feature is that the result optimization method is based on users’ feedback, which determines the relevance between Web pages and user query words. The returned pages with improved page ranks are directly mapped to the user feedbacks and dictate higher relevance than pages that exist in the result list. Bipartite graph technique can be employed on query logs to retrieve a better clustering of user queries and thus returning more efficient results.

[1] A. K Sharma, Neha Aggarwal, Neelam Dhuan and Ranjna Gupta, ”Web Search Result Optimization by Mining the Search Engine Query logs,”. Proceeding IEEE International Conference on methods and models in Computer Science (ICM2CS-2010). [2] A. Arasu, J. Cho, H. Garcia -Molina, A. Paepcke, and S. Raghavan, “Searching the Web,” ACM Transactions on Internet Technology, Vol. 1, No. 1, pp. 97_101, 2001. [3] A. Spink, D. Wolfram, B. J. Jansen, T. Saracevis,”Searching the Web: The public and their queries”. journal of the American Society for information Science and technology 52(3), 2001, 226-234. [4] R. Cooley, B. Mobasher and J. Srivastava, “Web mining: Information and pattern discovery on the World Wide Web,” In 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 97) 1997. [5] M. H. Dunham, Companion slides for the text,” Data mining Introductory and advanced topics”. Prentice Hall,2002. [6] J. Wen, J. Mie, and H. zhang,” Clustering user queries of a search engine”. In Proc of 10th International WWW Conference .W3C, 2001. [7] Thorsten joachims,”optimizing search engine using click though data” Proceeding of the 8th ACM SIGKDD international conference on [8] H.Ma, H.Yang,I.King,and M.R.Lyu,”learning latent semantic relations from click though data from query suggestion ”.InCIKM’08:Proceeding on the 17th ACM conference on information and knowledge management ,pages 709-708,New York,ny,USA,2008,ACM. [9] Isak Taka, Sarah Zelikovitz, Amanda Spink, “Web Search log to Identify Query Classification terms “Proceeding of IEEE International Conference on Information Technology (ITNG’07), pp: 50-57, 2008. [10] L. Page, S.Brin, R.Motwani, T.Winograd,”The page rank citration ranking: Bringing order to the Web”.Technical report, Stanford Digital Libraries SIDL-WP-1990-0120, 1999. [11] C. RIDINGS AND M.Shishigin,”Pagerank uncovered”. Technical report, 2002. [12] ]http://pr.efactory.de/e-pagerank-algorithm.shtml. [13] Neelam Duhan, A.K Sharma, “A Novel Approach for Organing Web Search Results using Ranking and Clustering,” International Journal of Computer Applications (0975-8887) Volume 5-No.10, August 2010.