Top PDF A Survey of Web Mining and Various Web Mining Techniques

A Survey of Web Mining and Various Web Mining Techniques

A Survey of Web Mining and Various Web Mining Techniques

Abstract: Mining is essence of valuable information from the huge set of raw information. Mining techniques in data mining is known as web mining. The rapidly increasing number of web contents including image, multimedia, and digital data. The knowledge gained from the web can be utilised for increase the performance for searching of data. In the internet there is so many duplicate data in present. Thus how we can utilise the useful data from the whole collection of data is a tough task.Web mining shows the past work on using different web algorithms. Today The huge amount of data is present on web. The web crawler plays the essential role in updating the current data. The search engine are depends on the ranking technology instead of the other vector based approaches. This paper will focuses on different web mining techniques and the various algorithm used in it.
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A Survey on Web Mining Techniques and Applications

A Survey on Web Mining Techniques and Applications

Abstract— Information on Internet and especially on Web sites increasing rapidly day by day, Web sites play an important role in this manner where a lot of Web users are always upload, download and brows a lot of contents based on their needs. Web server provide a way to browse these contents that scattered in many Web sites that reside in it by assigning an Internet Protocol (IP) address or Domain Name System (DNS) to be accessed around the world. Server log the requests that made by users to access their interesting content then store this information in the form of log file. Log file size can be growth from some kilobytes to several megabytes in few days depending on data traffic and the popularity of Web sites. With the fast growth of the data and information in Web environment made a necessity to use sophisticated techniques that have never used in other domains to extract knowledge and significant Web patterns. Web Mining is an extension of Data Mining that integrated various technology in research fields including Artificial Intelligence (AI), statistics, informatics, knowledge discovery and computational linguistics. The aim of Web Mining is to provide a algorithm or technique to make data accesses more efficient and convenient. Web Mining techniques are categorized into three classes depend on which part to be mined which are: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WSM), in this paper a survey of Web Mining techniques and application are discussed briefly.
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Comparative Analysis of Web Mining Techniques: Survey

Comparative Analysis of Web Mining Techniques: Survey

Abstract—The cluster of technologies and design are known as web, which has now emerged as a fertile area for data mining research. The web mining research is being carried out across the globe. Many research communities such as database information retrieval, AI, natural language are working on it. In this survey paper, I have discussed various researches in the area of web mining and have suggested three web mining categories. I have also tried to point out the relationship between the web mining and the agent. I have represented some issues, the process, algorithms and the application of the work.
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Web Mining Overview, Techniques, Tools and Applications: A Survey

Web Mining Overview, Techniques, Tools and Applications: A Survey

multiple sources, Knowledge/information filtering. Web mining with the owner-centric view allows getting Increasing contact / conversion efficiency (Web marketing), Targeted promotion of services, products, ads; Measuring the effectiveness of site content / structure, Providing dynamic personalized services or content. In the field of Customer analysis, it includes customer profitability, modeling customer behavior and reactions, customer satisfaction etc. Web mining in this field helps us to find strategy that should be used to get number of customers with quality as discussed in [1]. It is used to understand customer behavior, evaluate the effectiveness of a particular Web site, and help quantify the success of a marketing campaign [2, 5].Basically there are three sub categories for mining web information. These sub categories are
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Web Mining: A Survey on Various Web Page Ranking Algorithms

Web Mining: A Survey on Various Web Page Ranking Algorithms

The Intelligent surfer QD PageRank algorithm [17] is the improved upon the standard PageRank algorithm. It will introduce a more intelligent surfer model. The web surfers are mostly uses for the page to pages, they are also depends on the respective pages and their related contents or data. The user is request for a query for given websites web pages. When choosing between multiple out-links from a respective web page from the web resource that the intelligent surfer will select a random link from the set of pages where apply the query relevant, instead of one at random from the entire set of page out- links. It may be consider as an out links also for their purpose.
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11. A survey on Web Mining Techniques

11. A survey on Web Mining Techniques

ABSTRACT: As web is a vast collection of completely uncontrolled heterogeneous documents. Due to these characteristic, the web poses a fertile area of data mining research with the huge amount of information available online. This paper consists of brief description about existing methods for Web mining techniques. First section of the paper, consists of the concept of web mining & their categories. Secondly, it covers web mining techniques useful in search engines (like AltaVista , google) which has become inevitable part of our day to life. Lastly, a brief introduction about a Japanese search Engine- “Mondou (RCAAU)”, based on the emerging technologies of data mining have been discussed.
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A Survey on Various Techniques of Recommendation System in Web Mining

A Survey on Various Techniques of Recommendation System in Web Mining

Data mining is a process of extract useful information into a large dataset. The massive information available on the World Wide Web has search for data. To discover useful information from the web is called a web mining. E-commerce data are extract using a web mining. Web has deeply promoted the chance to create greater business opportunities and reach to customers easily. This 24*7 online service has resulted in the large amount of choices, but customers are now faced to information over-load. To overcome this problem Recommendation system is used.
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Web Content Mining Techniques: A Survey

Web Content Mining Techniques: A Survey

Several other techniques like Information retrieval, Information extraction and machine learning have been used in the past to discover the new knowledge from the huge amount of data available in the web. These techniques have been compared with web mining [15]. Information retrieval works by indexing text and then selects useful information [28]. Information Extraction focuses on extracting relevant facts whereas information retrieval selects relevant document. Web mining is now a part of Information retrieval system and Information Extraction system. IEhelps in preprocessing phase before web mining. It also helps in indexing which further helps in retrieval. Machine learning is not related to web mining directly but it supports web mining because it improves text classification process better than traditional Information Retrieval process [17]. Web mining is classified into the major three categories described in Fig. 1 [3].Web Content mining mines the content like text, image, audio, video, metadata, hyperlinks and extracts useful information. Since Web content mining examines the content of the web as well as the result of the search. Web Content mining mines the content like text, image, audio, video, metadata, hyperlinks and extracts useful information.
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Web Log Analyzer for Semantic Web Mining

Web Log Analyzer for Semantic Web Mining

The study was carried out in which comparative statements of various page ranking algorithms with link editing, General Utility Mining and Topological frequency Utility Mining. Researchers also provided Model by taking constraints such as Web Mining activity, topology, Process, Weighting factor, Time complexity, and Limitations etc. This also helped in comparing WPs-Tree and WPs-I tree structures. They concluded the page ranking algorithms play a major role in making the user search Navigation easier in the results of a search engine, which helps in best utilization web resources by providing required information to the Navigator. They also concluded WPs-Tree and WPs- I tree provide better storage representations. The association between web pages could be found easily in an efficient way. This survey could be helpful for understanding various page ranking algorithms along with different storage representation to correlate web pages. As a future direction, the new metric could be developed which may be still better than this, so that users could have quick response, resources on the network could be used efficiently thus promoting green computing. (Prasad Reddy, Shashikumar G.Totad, Geeta R. Bharamagoudar, Sept – Oct 2012).
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Model Survey on Web Usage Mining and Web Log Mining

Model Survey on Web Usage Mining and Web Log Mining

Abstract - At present in our day to day life internet plays a very important role. It has become a very vital part of human life. As internet is growing day by day, so the users are also expanding at much greater rate. Users spend lot of time on internet depending on the behavior of different user. Internet provides huge amount of information and from this information knowledge is extracted for the users. This extraction of information demands for the new logics and method. The data mining techniques and applications can be used in web based applications for performing this job which is also known as web mining. Web based mining or web usage mining is one of the trending topics nowadays. When user uses internet or visits some web pages, the associated information are stored in the server log files. Using these log files of server the human nature or behavior can be predicted. This paper focus on the web based mining and how it can be can be used to predict the human behavior using the server log files. The paper contains some of the techniques and methods associated with web mining.
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Preprocessing Techniques in Web Usage Mining: A Survey

Preprocessing Techniques in Web Usage Mining: A Survey

With the enormous growth of web there is a huge volume of structured, unstructured, semi- structured, heterogeneous, dynamic, distributed and high dimensional data available on web pages. So accessing relevant information with speed is a challenging task today. Several issues like multimedia data , scalability and temporal arises due to dynamic and diverse nature of data .While interaction with web various problems like finding useful information, personalization of information, to learn about consumers or individual users, creating new knowledge from the information available on web arises [1,2]. To solve these problems many techniques from Information retrieval (IR), Database, Natural Language Processing (NLP), Web mining are used directly or indirectly [4, 5]. Among them web mining has emerged as most popular and effective technique to overcome above problems in last few decades. Web mining is an application of data mining to extract uncover, relevant, hidden information on web. Web mining can be categorized into three classes based on content, structure and usage of web pages which is shown in Figure 1 [1, 27].
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Knowledge Extraction for Semantic Web using Web Mining with Ontology

Knowledge Extraction for Semantic Web using Web Mining with Ontology

This paper [5] proposes a method for making the K-Means algorithm more effective and efficient; so as to got better clustering with reduced complexity for discovering content from web pages using web content mining. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups, usually multidimensional is classified into groups (clusters) such that members of one group are similar according to a predefined criterion. The proposed algorithm uses standard deviation that reduces the time to make the cluster in simple k-mean. Tatyana IVANOVA Technical University of Sofia, College of Energy and Electronics, Botevgrad Bulgaria [6] proposed and discussed the architecture of the ontology learning module for extension of integrated development environment for learning objects, known also as Learning resource management and development system by integration of semantic technologies. The Authors Sivakumar and Ravichandran K.S As given in [7] semantic A Review on Semantic-Based Web Mining and its Applications. Author survey the Semantic-based Web mining is a combination of two fast developing domains Semantic Web and Web mining. Our approach is supported by our integrated the current challenges of the World Wide Web (WWW). The idea is to improve the results of Web Mining by making use of the new semantic structure of the Web and to make use of Web Mining for creating the Semantic Web.
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WEB CONTENT MINING METHODS AND APPLICATIONS FOR INFORMATION EXTRACTION: A SURVEY

WEB CONTENT MINING METHODS AND APPLICATIONS FOR INFORMATION EXTRACTION: A SURVEY

Abstract: Web Mining is extracting information from the web resources and finding out of the ordinary patterns that can be useful from ever expanding database of WWW. One of the subfield of Web mining is Web Content Mining. Web data processing is the technique of handling high volume of data. Web mining provides high performance system to the users to search for the content/data and obtains information of a particular content/data by searching through the servers that contains the sources. Web content mining is simply an integration of data from various website sources and aggregate from various websites using web content mining techniques. This paper presents a survey on web content mining techniques used for mining and application of web content mining and also shows some of the promising techniques used for extraction of data from various websites using web content mining techniques.
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A Survey of Issues and Techniques of Web Usage Mining

A Survey of Issues and Techniques of Web Usage Mining

With the brisk growth of the World Wide Web, the web has become an imperative medium of information dissemination. Therefore, the information available on the Web has become a vital source of information for the users of the internet. When data mining techniques are applied to Web data, it is referred to as Web mining. In 1996 its Etzioni [4] was first to coin the term web mining. For example in various websites contains various webpages and webpages having various pattern, through web mining we extract useful pattern from websites. According to analysis targets, web mining can be divided into three different types [9]
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Web Page Noise Removal - A Survey

Web Page Noise Removal - A Survey

Web mining is used to extract useful information from websites which includes web documents and hyperlinks of web sites. The World Wide Website contains a wide range of web pages which are very useful to many users. Web pages are composed of different kinds of data, such as text, audio, video and images. In addition to this, nowadays, web pages contain a large amount of unnecessary data, e.g., advertisement posters, navigation bars and disclaimer/copyright notices. These types of unnecessary data are called as noisy data. This has created the distractions to the user and also increases the time to perform searches and browsing tasks. To perform in-depth analysis of web data or web content mining, the first and essential step is to remove the noises which are existing in the web pages, and then we can extract useful information from the web pages. Removing noise from the web page is challenging task in web content mining. This main objective of this paper is to discuss the basics of web content mining, types of noises, techniques used for noise removal and different models used in the literature.
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Explorative Study of Web Data Mining Techniques and Tools: A Review

Explorative Study of Web Data Mining Techniques and Tools: A Review

Today World Wide Web (WWW) has become a complex universe as it updates regularly. WWW is basically a source of huge amount of information that provides all the needful sources of data mining [1]. WWW is a vast resource of multiple types of information in various formats which is very useful in the analysis of business progress that is very much important to stand in the competition of business now days. WWW is an online system that contains interlinked files such as images, videos, audios and other form of multimedia data [2]. Web data mining has been frequently used all over the world from a small scale business to a large scale business. This technique of data mining is used for web based applications and is the major need of each and every field. Web data mining is a term used for a technique, through which various web resources are used for collecting the useful information that makes it easy for an individual or a company for utilizing these resources and information in their best interest. One of the important challenges is to mining the web data as the data available on the World Wide Web is increasing continuously, thus it is difficult to retrieve information without data mining. Data Mining, usually called Web mining when applied to the Internet, is a process of extracting hidden predictive information and discovering meaningful patterns, profiles, trends from huge databases. Data mining of the World Wide Web is mainly designed for the comfort of the developers and the users of web data system. As a major source of information the web serves as a resource provider for the researchers of web data mining domain. Out of the given information deriving only the required information of data is the main target of web mining. WWW contains massive information which can be utilized easily by anyone, anywhere and anytime.
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A Survey on Methods used in Web Usage Mining

A Survey on Methods used in Web Usage Mining

Web usage mining is the process of withdrawing the useful knowledge from the server logs. It is the application of data mining techniques to discover interesting usage patterns from Web data in order to comprehend and better serve the requirements of the Web-based applications. Web usage data note down the identity of the user and their browsing behavior at a particular Web site. Usage data can be documented in the form of log files. A Web log is a file in which the server takes the knowledge/data each time a user requests a site from a particular server. A log file can be placed in three different locations i.e. web servers, web proxy server, user’s browser.
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Enhancing Performance of Web Page by Removing Noises using LRU

Enhancing Performance of Web Page by Removing Noises using LRU

techniques are like web mining, web content mining, text mining etc. Data mining is studied for different databases like object-relational databases, relational database, data ware houses and multimedia databases etc. The information extraction procedure in data mining consists of some steps from raw data collection to valuable information; which is shown in Figure 1. Data is collected from various sources, then that data is cleansed. In data warehouse, data from various sources is integrated into common source. Then the applicable data is selected to begin the process. Web Content Mining is the process of extracting useful information from the contents of Web. Many techniques from other disciplines are also used in research such as Information Retrieval and Natural Language Processing (NLP). Web Usage Mining is the application of data mining which is used to discover interesting usage patterns from Web data in order to understand and better serve the requires of Web-based applications [5]. The captured data or patterns then help to identify the origin of Web users along with their browsing behavior at a Web site. But a web user sometimes or we can say naturally ignores some parts of the web page which contains additional non- informative contents or which are not of the interest. This also makes it tough to discover main content of document. With the rapid expansion of information on World Wide Web, it becomes a popular place to extract information but also it is really difficult to identify the correct or relevant information because there are many distracting features available around the actual content of web pages. Useful information is surrounded by noises such as banners, advertisements etc, these noises effects web pages performance and efficiency. So, data mining becomes an interesting feature for discovering valuable information. In this work, we will focus on to removing the advertisements from the web pages to improve the performance of web mining which is the application of data mining techniques to extract knowledge from Web data including Web documents, hyperlinks, usage logs of web sites, etc.
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Mining Data Using Various Sequential Patterns Mining Algorithm in Semantic Web Environment

Mining Data Using Various Sequential Patterns Mining Algorithm in Semantic Web Environment

Semantic Web Mining is an integration of two important research areas: Semantic Web and Data Mining [1]. The existing Web (WWW) has a huge amount of information that is often unstructured and only human understandable. Web is rich with data; gathering and making sense of the information in the web is more difficult because the document of the Web is largely unorganized and unstructured. On the unstructured human readable web data, semantic web is used to effectively and efficiently creating a machine-understandable. In Semantic Web Mining, it refers to the application of data mining techniques to mine knowledge from World Wide Web [2] or the part of data mining that refers to the use of algorithms for extracting patterns fro m resources scattered over in the web.
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A SURVEY ON WEB CONTENT MINING

A SURVEY ON WEB CONTENT MINING

The concept of “WEB CONTENT MINING” involves techniques for summarizing, classification and clustering of the web contents. It is mainly based on research in information retrieval and text mining, such as information extraction, text classification and clustering, and information visualization. Ittargets the knowledge discovery, in which the main objects are the traditional collections of text documents and also the collections of multimedia documents such as images, videos, audios, which are embedded in or linked to the Web pages. Some of the prominent web content mining techniques are as follows:-
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