Web Data Mining

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The application of web data mining in personalized learning system

The application of web data mining in personalized learning system

Web data mining is a new technology that is the combination of Web and data mining, and it is a research hotspot of domestic and foreign scholars. There is a considerable difference of required learning information between learners in online education, and so the personalized information service has become a new service mode. Through personalized service, it can shorten the distance between learners and the education service organization and provide learners with more targeted services to improve learners’ service quality. The paper first analyzes the status of online education, and then put forward a personalized online education system model based on web data mining technology, and last introduces the basic designed idea of the model and the basic functions and implementation techniques of each module. This will play a positive role in promoting the development of personalized online learning.
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DIET RECOMMENDER SYSTEM USING WEB DATA MINING

DIET RECOMMENDER SYSTEM USING WEB DATA MINING

In this paper, a new framework based on data mining techniques is proposed to improve your health and avoid the types of foods that raise your risk for illnesses. The proposed framework is designed to enhance this interaction by analyzing user access behaviors on the system. In addition to the content analysis (i.e., content- based filtering)information is also retrieved according to each individual’s preferences (i.e. user personalization) and by recommendation from other users (i.e. collaborative filtering).We suppose that there is a website where people could take their orders over the internet just like that in the restaurant. We acquire people eating habit data in the database which could track people’s recipe record. Also people could input their eating data into the database through the website. Then we introduce a web data mining solution to e-commerce to discover hidden patterns and business strategies from their customer and web data, propose a new framework based on data mining technology for building a Web-page recommender system, which would be used as the basic frame work for healthy eating system. Finally we give out personalized recommendations for each person.
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Web Data Mining Techniques and Implementation for Handling Big Data

Web Data Mining Techniques and Implementation for Handling Big Data

Abstract- Nowadays, Web becomes an important part of organization. The huge usage data as a result of interaction of users and Web can be extracted to be knowledge applied in various application. Analysis of web site regularities and patterns in user navigation is getting more attention from business and research community a web browsing becomes an everyday task for more people around the world. This extremely large-scaled data called Big data are in terms of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. The size of the collected data about the Web and mobile device users is even greater. To provide the ability to make sense and maximize utilization of such vast amounts of web data for knowledge discovery and decision-making is crucial to scientific advancement; we need new tools for such a big web data mining. Visualization is a tool, which is shown to be effective for gleaning insight in big data. Apache Hadoop and other technologies are emerging to support back-end concerns such as storage and processing, visualization- based data discovery tools focus on the front end of big data on helping businesses explore the data more easily and understand it more fully.
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Study & Analysis of Web Content Mining Tools to Improve Techniques of Web Data Mining

Study & Analysis of Web Content Mining Tools to Improve Techniques of Web Data Mining

As websites are a key communication channel not only for companies, but also for private individuals trying to find diverse information, it is important to find ways to make the web more usable. A website is a collection of related web pages containing images, videos or other digital assets [1]. In order to, for example, understand user behaviour or results of search engines it is necessary to analyse the information available on the Web. The field that describes these tasks is called Web Mining. World Wide Web is an evolving system of interlinked files like containing audio, images, videos, and other multimedia. The term Web Data Mining is a technique used to crawl through various web resources to collect required information, which enables an individual or a company to promote business, understanding marketing dynamics, new promotions floating on the Internet, etc. There is a growing trend among companies, organizations and individuals alike to gather information through web data mining to utilize that information in their best interest. The Web contains massively information and provides an access to it at any place at any time. Most of the people browsing the internet for retrieving information, but most of the time, they get lots of insignificant and irrelevant document even after
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Research Issues in Web Data Mining

Research Issues in Web Data Mining

mining the web document’s structures and links. In [24], some insight is given on mining structural information on the web. Our initial study [5] has shown that web structure mining is very useful in generating information such visible web documents, luminous web documents and luminous paths; a path common to most of the results returned. In this paper, we have discussed some applications in web data mining and E-commerce where we can use these types of knowledge. Web content mining describes the automatic search of information resources available on-line. Web usage mining includes the data from server access logs, user registration or profiles, user sessions or transactions etc. A survey of some of the emerging tools and techniques for web usage mining is given in [2]. In our discussion here, we focus on the research issues in web data mining with respect to the web warehousing project called WHOWEDA (Warehouse of Web Data).
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Implementing Web Data Mining Framework for Online Clothes Shopping

Implementing Web Data Mining Framework for Online Clothes Shopping

The inner part of characteristic action of web data mining are applying various methods and algorithms which will help in order to discover and extract excellent examples or various of stored data [6]. From the previous few decades data mining and knowledge based discovery applications have gained wealthy focus due to its suggestive meaning in decision making and it has become a most essential component in various organizations and sectors [6]. The field of web data mining have been succeeded and helped in extracting into new areas of human life with various constitutes and went forward in the fields of Statistics, Databases, Machine based Learning, excellent example Reorganization, Artificial Intelligence and Computation based capabilities etc... Web mining technique can be widely classified into three domains: content, structure and usage mining. The actual study of three domains is described below.
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Title: Efficient Web Data Mining with Standard XML Technologies

Title: Efficient Web Data Mining with Standard XML Technologies

Data validation is performed on several levels of abstraction. Syntactic checks are performed first: they verify that each XML element is present in the output and that values match their expected types (numeric vs. string). This is followed by semanti c checks which spot incorrect values. This is domain-specific but very powerful. For instance, if it is known that stock prices are usually less than $ 1000 (Berkshire-Hathaway shares being the notable exception), this can be described to the data validator which then separates the “bad” data from “good.” The bad data is moved to a staging area and the administrator is asked to decide what to do with it. The administrator can accept the data as-is, the boundary conditions can be automatically modified, the data can be ignored as a one-time error, or the data can be manually corrected.
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The Optimization and Improvement of MapReduce in Web Data Mining

The Optimization and Improvement of MapReduce in Web Data Mining

Extracting and mining social networks information from massive Web data is of both theoretical and practical significance. However, one of definite features of this task was a large scale data processing, which remained to be a great challenge that would be addressed. MapReduce is a kind of distributed programming model. Just through the implementation of map and reduce those two functions, the distributed tasks can work well. Nevertheless, this model does not directly support heterogeneous datasets processing, while heterogeneous datasets are common in Web. This ar- ticle proposes a new framework which improves original MapReduce framework into a new one called Map-Reduce-Merge. It adds merge phase that can efficiently solve the problems of hetero- geneous data processing. At the same time, some works of optimization and improvement are done based on the features of Web data.
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An average linear time algorithm for web data mining

An average linear time algorithm for web data mining

In [14] the authors propose a novel data mining model specific to analyze log data. A log data mining system is devised to find patterns with predefined characteristics by means of a query language to be used by an expert. Several authors have proposed the use of Markov models to model user requests on the web. Pitkow et al. [12] proposed a longest subsequence model as an alterna- tive to the Markov model. Sarukkai [13] uses Markov models for predicting the next page accessed by the user. Cadez et al. [6] use Markov models for classifying browsing sessions into different categories. Deshpande et al.[7] pro- pose techniques for combining different order Markov models to obtain low state complexity and improved accuracy. Finally, Dongshan and Junyi [8] proposed an hybrid-order tree-like Markov model to predict web page access which pro- vides good scalability and high coverage. Markov models have been shown to be suited to model a collection of navigation records, where higher order models present increased accuracy but with a much larger number of states.
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Elimination of Redundant Information for Web Data Mining

Elimination of Redundant Information for Web Data Mining

Nowadays, a great number of web documents are created and published to the Internet world with a variety of styles and information for users. The content from Web documents has lots of information from different types of fields and they are designed for diverse groups of aimed users. Thus, Web content’s extraction and analysis become more challenging due to the growing number of online accessible Web documents and the density of the content’s organization. The pages use a more complex structure for the design and present their information in various Web styles using numerously available Web editors. Web content’s extraction and analysis are very important processes in the information retrieval system, Web classification and monitoring system. Due to the current trend of layout design of Web pages, most information is divided in according to contents’ nature: navigation, main content, auxiliary information. Out of the various layout practices, the more accurate detection of main content area means the more accurate classification or monitoring of information.
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Title: Application for Data Mining and Web Data Mining Challenges

Title: Application for Data Mining and Web Data Mining Challenges

4.5. Dynamic web Mining: Web mining can even acknowledge as dynamics web. However the web changes within the perspective of its material, components, and accessibility designs. Saving sure items of ancient details associated with these web exploration aspects helps in discovering changes in material and linkages. During this case, we are able to valuate photos from totally different time postage stamps to acknowledge the up-dates. However, as opposition relative knowledge supply systems, the Internet's wide depth and enormous look of details produce it nearly tough to systematically look past photos or upgrade records. These restrictions produce discovering such changes typically unworkable. Mining Web accessibility activities, on the opposite hand, is each potential and, in several programs, quite helpful. With this strategy, customers will mine blog data to get website access designs. Assessing and discovering regularities in blog data will enhance the standard and distribution of web data services to the top individual, enhance web hosting server system performance and acknowledge customers for electronic trade. An online hosting server sometimes signs up an online log entry for each website accessed. This accessibility includes the asked for uniform resource locator, the science address from that the request is started, and a time seal. Web-based e-commerce hosts gather several Web accessibility log details. Fashionable websites will register blog details that selection many mega bytes daily. Blog directories offer made details concerning web characteristics. Gap these details needs innovative blog exploration techniques. The success of such programs depends on what and the way a lot of legitimate and economical data we are able to verify from the raw details. Often, researchers should clean, reduce, and convert these details
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A SURVEY ON DATA MINING FOR SEMANTIC WEB DATA

A SURVEY ON DATA MINING FOR SEMANTIC WEB DATA

Semantic web works in smarter way as it provide web service, which synchronizes and arranges all the data over web correctly and in a disciplined manner. With the success of the World Wide Web (WWW) addresses new challenge as the amount of data is so huge. The Semantic Web addresses the part of this challenge by trying to make the data machine understandable, and Web Mining addresses the other part by extracting the useful knowledge hidden in these data [4].Semantic Web Mining aims at combining the two areas Semantic Web and Web Mining along with data mining. As there is increase in the numbers of resercher work on improving the quality of Web data Mining by exploiting semantics in theWeb data, and using mining techniques for building the Semantic Web.
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WEB MINING TECHNIQUES PERFORMANCE IN KNOWLEDGE DISCOVERY AND DATA MINING: A STUDY

WEB MINING TECHNIQUES PERFORMANCE IN KNOWLEDGE DISCOVERY AND DATA MINING: A STUDY

Consequently, separating valuable information is a key testing issue in web data mining. The billions of Web pages made are produced progressively by basic Web database service motors utilizing HTML or XML. Be that as it may, looking, understanding, and utilizing the semi- organized information put away on the Web represents a critical test since this data is more refined and dynamic than the information that business database systems store — the mining data shifts from organized to unstructured. Data mining, for the most part, manages organized data sorted out in a database while content mining primarily handles unstructured data. Web mining lies in the middle of and adapts to semi-organized data or potentially unstructured data. Web mining calls for innovative utilization of data mining or potentially content mining techniques and its unmistakable methodologies[2-7].
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A Novel Data Mining Algorithm for Semantic Web Based Data Cloud

A Novel Data Mining Algorithm for Semantic Web Based Data Cloud

In this paper we described the data mining approach applied to the data cloud spread across the globe and connected to the web. We used the Sector/Sphere framework integrated to association rule based data mining. This enables the application of the association rule algorithms to the wide range of Cloud services available on the web. We have described a cloud- based infrastructure designed for data mining large distributed data sets over clusters connected with high performance wide area networks. Sector/Sphere is open source and available through Source Forge. The discovery of the association rule is a most successful and most vital duty in the data mining, is a very active research area in current data mining, its goal is to discover all frequent modes in the data set, and the current research work carrying on are mostly focused on the development of effective algorithm. On the basis of in-depth study of the existing data mining algorithms, in this paper a new data mining algorithm based on association rules is presented. The algorithm can avoid redundant rules as far as possible; the performance of the algorithm can be obviously improved when compared with the existing algorithms. We are implementing the algorithm and comparing our algorithm with other approaches.
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Novel Web Usage Mining for Web Mining Techniques

Novel Web Usage Mining for Web Mining Techniques

Web usage mining data is related to mainly users‟ navigation on the web. The most common action of the web user is navigation through web pages by using hyperlinks. A web page can be accepted as related to another web page if they are accessed in the same user session; also, similarity increases if both of these pages are accessed in the same navigation of a user. However, since http protocol is stateless and connectionless, it is not easy to discover user sessions from server logs. For reactive strategies, all users behind a proxy server will have the same IP number and will be seen as a single client and all of these users‟ logs will contain the same IP number in the web log data. Also, caching performed by the clients‟ browsers and proxy servers make web log data less reliable. These problems can be handled by proactive strategies by using cookies or java applets. However, some clients could have disabled these solutions easily. In this case proactive strategies become unusable.
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Generating Vague Neighbourhoods through Data Mining of Passive Web Data

Generating Vague Neighbourhoods through Data Mining of Passive Web Data

The appropriateness of a Chi-square test is dependant on having enough data points. Given the small numbers associated with individual postcodes (approximately 15 prop- erties), it is highly likely that, given response rates and dividing data points between a number of differing neighbourhoods, there would be insufficient data points for a Chi- square test at the postcode level. Thus, in order to meet the assumptions of the test, data were aggregated for all three postcodes in order to investigate any differences between the output from the web extraction method and resident responses. Data aggregation in this case was acceptable due to the close proximity of the postcodes, with them covering an area of just over one hectare in size. No statistically significant difference was found between the web extraction output and resident responses (X 2 = 0.09, df = 2, p < 0.05). This has two important implications as it not only aids the validation of the web ex- traction output (being similar to that when asking residents directly) but also more fundamentally supports the notion and requirement of vague neighbourhood boundaries. If a more accurate representation is required - the fact that people even within the same property can have such varying views 1 demonstrates that discrete boundaries can never capture such complexity and vague notions are necessary.
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Generating vague neighbourhoods through data mining of passive web data

Generating vague neighbourhoods through data mining of passive web data

The first requirement of the method was to extract postal addresses from mass text corpora that reveal common usage (in our case this aggregated corpus is the Web, but this could easily be extended to include many more forms of data source). Within the UK, an official postal address in an urban area (set by Royal Mail - the dominant postal service company in the UK) is defined as: building number and street ; city; postcode. It is this format that is searched for. The underpinning assumption is that, even though official urban addresses in the UK do not require any information to be entered between the street and city elements, people often interleave neighbourhood names within that structure (see Figure 1). This is then the source of our neighbourhood knowledge. Whilst such an approach is well suited to UK addressing conventions, the application within other countries may be problematic. This issue is addressed within the discussion section. To determine what to search for, we first obtain a set of postcodes via OS’s Code-Point Open dataset. These are automatically iterated through the Bing API, with relatively simple linguistic pattern matching techniques being applied to the returned results in order to: (1) extract postal addresses from each document; (2) apply rule based filters to identify street and city names (either via identification in Royal Mail’s Postal Address File lists, or through detection via common suffixes and abbreviations (such as rd or st); (3) to then extract the text between these two entities, pairing the resulting neighbourhood candidate names with the current postcode. This process produces a set of <postcode, neighbourhood> pairs.
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Web Mining for Web Personalization

Web Mining for Web Personalization

. The data mining methods that are employed are: association rule mining, sequential pattern discovery, clustering, and classification. This knowledge is then used from the system in order to personalize the site according to each user’s behavior and profile. The block diagram illustrated in Figure 1 represents the functional architecture of a Web personalization system in terms of the modules and data sources that were described earlier. The content management module processes the Web site’s content and classifies it in conceptual categories. The Web site’s content can be enhanced with additional information acquired from other Web sources, using advanced search techniques. Given the site map structure and the usage logs, a Web usage miner provides results regarding usage patterns, user behavior, session and user clusters, clickstream information, and so on. Additional information about the individual users can be obtained by the user profiles. Moreover, any information extracted from the Web usage mining process concerning each user’s
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A Survey of Web Mining and Various Web Mining Techniques

A Survey of Web Mining and Various Web Mining Techniques

Web structure mining known as linkage construction analysis, it is the area of web mining. Different ways on huge hyperspace linkage storeroom to prompt systematic way regarding websites as well as webpages by analysing the link association that can be used for increasing the page hits and to improve page ranking. The data come from web structure mining includes of textual webpages assembled by slowpoke from all above the connection of multiple server. It contains the four basics tide.

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

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.
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