Application Of Web Usage Mining Research Articles

Technological Prospects of Cloud Computing in Web Mining: Recent Trends and Opportunities

Web has immense potentials to grow with new technologies and flourish with new opportunities in almost every sphere of human life. The internet grew by WWW and explored with e-business and social network. The Web is known and participating in mining of various kinds of data ranging from users views and patterns to Bitcoin applications. Web mining includes different set and types of data and extract useful information and from various sources of web and gain knowledge using applicable data mining technique on dataset. Web mining types broadly categorised in three main areas, web uses mining, web content mining, and web structured mining. Each category of web mining is involved to handle issues of heterogeneous behaviour of web data. All three technique and applications are highly required of high end architectures which can facilitate infrastructures and support for all required criteria. Cloud computing is an emerging technology and blowing with intensive support for varieties of applications. The techniques and applications of Web usage mining are extremely demanded in cloud computing. Cloud computing allow these technique to retrieve relevant and useful data through virtually integrated mode of data warehouse. It helps the users to reduce cost and infrastructure for implementation. This paper presents methodologies of web mining using Cloud Computing technology and its prospects.

Automated User Behavior Mapping Using Web Usage Mining

Automated User Behavior Mapping is an application of web usage mining using which we can see the real-time behavior of end user visiting a particular web page automatically. The technologies used in this are socket programming for real-time communication between the server and the user accessing the website for collection of web log data and selenium web driver for automating the user behavior using web log files.

Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

Conceptualizing Mining of Firm’s Web Log Files

AbstractIn this era of a data-driven society, useful data (Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information, however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

An Approach Proposed for Detecting Users activities from Recorded Log

The development of the web has created a big challenge for directing the client to the website pages in their area of interest. Accordingly, just option is to capture the intuition of the client and provide them a list of recommendation. Most specifically, online navigation activities develop with day by day; consequently extract information with the capability of intelligence, from these activities is a tedious job. Webmaster of an organization ought to utilize methods of web mining to fetch intuition, Web usage mining (WUM) is one among them.WUM is designed to operate on web server logs; logs contain client's navigation history which is very useful for the web recommendation. Recommendation is an application of web usage mining. Consequently, recommendation system can be utilized to forecast the navigation pattern of client and recommend those to client in a form of recommendation list. This paper, suggest a recommendation principal that recommends a list of pages on the basis of client's past navigation history (recorded within the web log). This approach brings the advance within the precision of displayed pages for the client or users.

A Review on Current Recommendation Systems

In recent years, E-commerce, web service and web information system have been used explosively. Massive explosion of the world-wide-web and the emergence of ecommerce have encouraged designers to develop recommendation systems. Web users demonstrate a variety of navigational patterns by clicking series of web pages. These patterns can be understood by mining user logs using WUM. One of the widely used applications of Web Usage Mining is Online Recommendation and prediction. Generally, all the recommendation systems follow a framework for generating efficient recommendations. Various recommendation systems use different approaches based on the sources of information they utilize. The accessible sources are user information (demographics), the product information (keywords, genres) and the user-item ratings. This paper gives introductive information about recommendation system, their techniques, and algorithms and also describes some existing works.

Optimising web usage mining for building adaptive e-learning site: a case study

An important application of web usage mining is mining web log data. We propose a new optimised technique for web mining, in the realm of an e-learning site to recommend the best links for a learner to visit the next. It optimises web mining, by partitioning the database, on the basis of the learner's knowledge level, to create a suffix tree(s) from the existing sequences of previous 'n' learners' path. To further reduce the overhead of re-mining the web patterns, we propose that a web traversal pattern should be regarded as significant, only if it qualifies the minimum threshold of length and frequency in the database. These significant patterns are added to suffixes. They are then mined, using the most efficient mining algorithm after a comparative analysis of various algorithms, to find the most frequent navigation paths for recommendation to n + 1th new learner. We conducted experiments on a real case study of an Indian e-learning site. This is verified by experiments with promising results on computational time. This speed up obtained, in web pattern mining, is a meaningful approach for building recommender based e-learning system.

Web Recommendation Framework based on Association Rules Coverage to be Applied for Site Modification

This paper introduces a Web Recommendation Framework based on the usage history to be applied for Site Modification as one of the applications of Web Usage Mining (WUM) that is applicable for online business and marketing applications. The framework focuses on the three main interdependent tasks for performing WUM which are Preprocessing, Pattern Discovery and Pattern Analysis. In Preprocessing, we remove all irrelevant users’ requests from the web server log file leading to log reduction followed by users’ identification then sessions’ identification. We take into consideration the ambiguity found in some researches concerning the HTTP common methods. In Pattern Discovery, we extract Association Rules that can be used to relate pages that are most often referenced together in a single server session and may not be directly connected to one another via hyperlinks. In Pattern Analysis, we analyze the set of generated rules independent of the website’s topology to extract valid set of rules that achieves highest coverage for the dataset. Our experimental results confirmed that calculating association rules coverage in our case study can lead to the best rules to be provided as recommendations that can help Web designers to restructure their Web site, Web applications or even portals to better serve Web customers. General Terms Web Mining, Web Usage Mining

Web Usage Mining: An Analysis

Web usage mining is research area in web mining. Web mining is an activity that focuses to discover new, relevant and reliable information and knowledge by examining the structure, content and usage of web. The major focus is on learning about web users and their interaction with websites. Web log files generated on web servers are used in order to extract web usage of different users. There are three types of web repositories: web server log, proxy server log, browser log. Analysing web logs for usage can not only provide important information to websites developers but also help in creating adaptive web sites. In this paper we discuss various sources of information for WUM, Methodology of web usage mining techniques which involves Data collection , Data pre-processing , knowledge discovery and knowledge analysis. Various applications of WUM are personalization, prefetching and caching, support to design and E-commerce. Major application of web usage mining is to predict future accesses. Thus, the result obtained after web usage mining can be used to improve the performance of prefetching and caching.

Process of Web Usage Mining to find Interesting Patterns from Web Usage Data

The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to users’ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.

A Personalized Product Based Recommendation System Using Web Usage Mining and Semantic Web

To be globally competent and competitive a successful presence on the web is necessary to sustain and retain itself in the market. The WWW is an interesting area for data mining because of abundance of information. Web users exhibit a variety of navigational interests through clicking a sequence of web pages. Analysis of this data will lead to discover many interesting patterns and facilitate users to locate more preferable web pages. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. The semantic information of the Web page contents is generally not included in Web usage mining. Online recommendation and prediction is one of the web usage mining applications. In this paper we present architecture for integrating semantic information about the products with web log data and generate a list of recommended products by using LCS Algorithm.

Analysis of Users’ Web Navigation Behavior using GRPA with Variable Length Markov Chains

the never-ending growth of Web services and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached enormous proportions. Analyzing such huge data can help to evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, and provide more personalized content to visitors. In the previous work, we had proposed a method, Grey Relational Pattern Analysis using Markov chains, which involves to discovering the meaningful patterns and relationships from a large collection of data, often stored in Web and applications server access logs, proxy logs etc. Herein, we propose a novel approach to analyse the navigational behavior of User using GRPA with Variable-Length Markov Chains. A VLMC is a model extension that allows variable length history to be captured. GRPA with Variable- Length Markov Chains, which reflects on sequential information in Web usage data effectively and efficiently, and it can be extended to allow integration with a Web user navigation behavior prediction model for better Web Usage mining Applications.

Improving Effectiveness of Online Teaching (An Application of Web Usage Mining)

WWW is one of the most valuable resources of information retrievals and knowledge discoveries and it supports Online teaching learning process. We can use this resource to upload information about particular subject (under course) and can find popularity of such information. We can extract knowledge from this resource by many mining techniques to improve the performance of this information. In this paper we are providing an introduction of web mining and review of web mining categories. Then we are focusing on one of these categories the web usage mining. After this we are showing how this category can be used to raise the performance of our information. General Term: Online teaching, Web usage mining

Differential Internet Behavior’s of Students from Gender Groups

World Wide Web (WWW) contains huge amount documents with various types of categories. Internet usage behaviors of users in Internet are one of the main research areas. Many Website classification schemes were present in literature based on different perspectives. For categorizing Websites based on its content, we used a classification scheme [1]. This investigation is the application of Web Usage Mining (WUM) and Education Data Mining (EDM) to differentiate students Internet usage behaviors based on their gender group.

COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY

A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection of Livelink log data. Livelink is a web-based product of Open Text Corporation, which provides automatic management and retrieval of different types of information objects over an intranet, an extranet or the Internet. We report our experience in preprocessing raw log data, mining association rules and sequential patterns from the log data, and identifying interesting rules and patterns by use of interestingness measures and some pruning methods. In particular, we evaluate a number of interestingness measures in terms of their effectiveness in finding interesting association rules and sequential patterns. Our results show that some measures are much more effective than others.

Classification of large data sets with mixture models via sufficient EM

For the classification of very large data sets with a mixture model approach a two-step strategy for the estimation of the mixture is proposed. In the first step data are scaled down using compression techniques. Data compression consists of clustering the single observations into a medium number of groups and the representation of each group by a prototype, i.e. a triple of sufficient statistics (mean vector, covariance matrix, number of observations compressed). In the second step the mixture is estimated by applying an adapted EM algorithm (called sufficient EM) to the sufficient statistics of the compressed data. The estimated mixture allows the classification of observations according to their maximum posterior probability of component membership. The performance of sufficient EM in clustering a real data set from a web-usage mining application is compared to standard EM and the TwoStep clustering algorithm as implemented in SPSS. It turns out that the algorithmic efficiency of the sufficient EM algorithm is much more higher than for standard EM. While the TwoStep algorithm is even faster the results show a lack of stability.

NP-miner: A real-time recommendation algorithm by using web usage mining

Web usage mining is widely applied in various areas, and dynamic recommendation is one web usage mining application. However, most of the current recommendation mechanisms need to generate all association rules before recommendations. This takes lots of time in offline computation, and cannot provide real-time recommendations for online users. This study proposes a Navigational Pattern Tree structure for storing the web accessing information. Besides, the Navigational Pattern Tree supports incremental growth for immediately modeling web usage behavior. To provide real-time recommendations efficiently, we develop a Navigational Pattern mining (NP-miner) algorithm for discovering frequent sequential patterns on the proposed Navigational Pattern Tree. According to historical patterns, the NP-miner scans relevant sub-trees of the Navigational Pattern Tree repeatedly for generating candidate recommendations. The experiments study the performance of the NP-miner algorithm through synthetic datasets from real applications. The results show that the NP-miner algorithm can efficiently perform online dynamic recommendation in a stable manner.

A Classification of Techniques for Web Usage Analysis

Techniques which assist in recognizing various access patterns and interests of the Web users are normally referred to as an application of Web usage mining. The application provides useful insights to address crucial points such as user interests into a particular page, server load balancing, Web site reorganization, clustering of similar browsing patterns, classification of user profiles, content caching or data distribution and replication. In this study, we provide an updated focused survey of major aspects and problems related to the task of modeling the user behavior. We also point out some recent advancement in areas related to automatic Web navigation and implicit capturing of user interests in regard to a particular page. Main future directions are also outlined in the conclusion.

A methodology for web usage mining and its application to target group identification

Web usage mining is an important and fast developing area of web mining where a lot of research has been done already. Recently, companies got aware of its potentials, especially for applications in marketing. A structured methodology is, however, a crucial requirement for a successful practical application of web usage mining. This publication provides such a methodology that is based on suggestions from literature and own experience from various web mining projects. Its application in a Chilean bank shows how a combined use of data from a data warehouse and web data can contribute to improve marketing activities. The benefits from this project point at the huge potential web usage mining has not only in financial services.

Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce

The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Collaborative filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This paper proposes a recommendation methodology based on Web usage mining, and product taxonomy to enhance the recommendation quality and the system performance of current CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, thereby leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than other CF methodologies.