Web Pages Ranking Algorithms: A Survey

Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.

Performance Comparison of Data Mining Classifiers on Web Log Data

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING

With the rapid increase in internet technology, users get easily confused in large hypertext structure. The primary goal of the web site owner is to provide the relevant information to the users to fulfill their needs. In order to achieve this goal, they use the concept of web mining. Web mining is used to categorize users and pages by analyzing the users‟ behaviour, the content of the pages, and the order of the URLs that tend to be accessed in order. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. It is very difficult for a user to find the high quality information which he wants. Page Ranking algorithm is needed which provide the higher ranking to the important pages. In this paper, we discuss the improvement of Page ranking algorithm to provide the higher ranking to important pages. Most of the search engines are ranking their search results in response to user’s queries to make their search navigations easier.

A Novel Approach of Development of Web Pattern by Focusing on Web Structure Mining Techniques

The World Wide Web is a very useful and interactive resource of information like hypertext, multimedia etc. When we search any information on the Google, there are many URL’s has been opened. The bulk amount of information becomes very difficult for the users to find, extract and filter the relevant information, so that some techniques are used to solve these problems. The objective of current manuscript is focus on processing of structured and unstructured data mining. With the tremendous growth in website, web portal to provide downloaded data to the user. The semantic web is about machine-understandable web pages to make the web more intelligent and able to provide useful services to the users. The data structure definition and recognition is to estimate the accurate page ranking and to produce better result while searching operation with web data.

Web Site Ranking Feedback System

We all search on google for something and get the results in the form of different websites with some description. We generally click on first or second website links if results are not found we go down on google page. The website ranking is given by search engines by different criteria. Last website won’t be seen by none of the people in most cases and first website will be having a great market compared to last website. So we need to help the last website to be moved up in results and help in generating revenue and have good rank on searching by giving feedback. This system will provide the difference between the first website and last website on the google results and will provide the feedback to the last website like content, links, images used by first website which helps the last website to be used in his webpage. This system is user friendly which is built on HTML as front end and Python flask as back end and used python package beautiful soup to parse HTML data and to automate browser behaviour with python. This system is done on web mining which has three categories firstly web content mining in which we scan the web pages and get to know the links, text, images used. Secondly web usage mining in which reports are generated after analysis which contain the details of text, images, links. Finally the web structure mining states that structural summary of website

Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

Optimizing webpage relevancy using page ranking and content based ranking

Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: Web structure mining, Web utilization mining, and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning taking into account the page content investigation. The strategy, we call it Page Content Rank (PCR) in the paper, consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms which the page contains. The significance of a term is determined concern-ing a given inquiry q and it depends on its measurable and linguistic elements. As a source set of pages for mining we utilize an arrangement of pages reacted by a web search tool to the question q. PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework –page rank algorithm.