Evaluation of usage patterns for web-based educational systems using web mining

Virtual courses often separate teacher and student physically from one another, resulting in less direct feedback. The evaluation of virtual courses and other computer-supported educational systems is therefore of major importance in order to monitor student progress, guarantee the quality of the course and enhance the learning experience for the student. We present a technique for the usage evaluation of Web-based educational systems focussing on behavioural analysis, which is based on Web mining technologies. Sequential patterns are extracted from Web access logs and compared to expected behaviour

iWAP: ASingle Pass Approach for Web Access Sequential Pattern Mining

With the explosive growth of data availability on the World Wide Web, web usage mining becomes very essential for improving designs of websites, analyzing system performance as well as network communications, understanding user reaction, motivation and building adaptive websites. Web Access Pattern mining (WAP-mine) is a sequential pattern mining technique for discovering frequent web log access sequences. It first stores the frequent part of original web access sequence database on a prefix tree called WAP-tree and mines the frequent sequences from that tree according to a user given minimum support threshold. Therefore, this method is not applicable for incremental and interactive mining. In this paper, we propose an algorithm, improved Web Access Pattern (iWAP) mining, to find web access patterns from web logs more efficiently than the WAP-mine algorithm. Our proposed approach can discover all web access sequential patterns with a single pass of web log databases. Moreover, it is applicable for interactive and incremental mining which are not provided by the earlier one. The experimental and performance studies show that the proposed algorithm is in general an order of magnitude faster than the existing WAP-mine algorithm

Recommendation Based On Comparative Analysis of Apriori and BW-Mine Algorithm

With The Growth of WWW recommending appropriate and relevant page to the user is a challenging task. In many web Applications, user would like to get recommendation based on their interest of surfing. Web Mining is used to extract relevant information for the user from logs, web content, hyperlinks etc. In this paper we will be using logs to recommend frequent access patterns to the users .This paper aims at using the logs of user ,cleaning logs , identifying users , identifying session , completing sessions from website structure and then using and comparing different recommendation algorithm like Apriori Algorithms and BW-Mine to recommend frequent items to the user. We will also be comparing different recommendations Algorithm with the help of example. The fundamental of finding access patterns with Apriori is that any set that occurs frequently must have its frequent subset. The fundamental of finding access pattern with BW-Mine, it constructs the WB-table, VI-List, and HI-Counter for finding frequent patterns

Data preperation and pattern discovery for web usage mining

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, these server logs cannot be used directly for patter discovery and analysis purposes. There are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. The objective of this paper is to discuss several data preparation techniques in order to identify unique users and user sessions. New heuristics to identify user sessions have been proposed. Also the data mining algorithms that can be applied to this processed data to discover patterns and rules have been discussed. On the basis of implementation of these algorithms, a comparative analysis among some of these algorithms is drawn on a 2-dimensional graph

Knowledge Discovery from Web Logs - A Survey

Web usage mining is obtaining the interesting and constructive knowledge and implicit information from activities related to the WWW. Web servers trace and gather information about user interactions every time the user requests for particular resources. Evaluating the Web access logs would assist in predicting the user behavior and also assists in formulating the web structure. Based on the applications point of view, information extracted from the Web usage patterns possibly directly applied to competently manage activities related to e-business, e-services, e-education, on-line communities and so on. On the other hand, since the size and density of the data grows rapidly, the information provided by existing Web log file analysis tools may possibly provide insufficient information and hence more intelligent mining techniques are needed. There are several approaches previously available for web usage mining. The approaches available in the literature have their own merits and demerits. This paper focuses on the study and analysis of various existing web usage mining techniques

Expectation maximization clustering algorithm for user modeling in web usage mining system

To provide intelligent personalized online services such as web recommender systems, it is usually necessary to model users’ web access behavior. To achieve this, one of the promising approaches is web usage mining, which mines web logs for user models and recommendations. Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this study we advance a model for mining of user’s navigation pattern. The model is based on expectation-maximization (EM) algorithm and it is used for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. The experimental results represent that by decreasing the number of clusters, the log likelihood converges toward lower values and probability of the largest cluster will be decreased while the number of the clusters increases in each treatment. The results also indicate that kind of behavior given by EM clustering algorithm has improved the visit-coherence (accuracy) of navigation pattern mining

Clustering of Web Users Using Session-based Similarity Measures

One important research topic in web usage mining is the clustering of web users based on their common properties. Informative knowledge obtained from web user clusters were used for many applications, such as the prefetching of pages between web clients and proxies. This paper presents an approach for measuring similarity of interests among web users from their past access behaviors. The similarity measures are based on the user sessions extracted from the user\u27s access logs. A multi-level scheme for clustering a large number of web users is proposed, as an extension to the method proposed in our previous work (2001). Experiments were conducted and the results obtained show that our clustering method is capable of clustering web users with similar interest

Log Mining Using Generalized Association Rules

Explosive growth in size and usage of the World Wide Web has made it necessary for Web site administrators to track and analyze the navigation patterns of Web site visitors. To achieve this goal, the use of web mining tool is necessary. Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents. Since Data Mining is primarily concerned with the discovery of knowledge and aims to provide answers to questions that people do not know how to ask, it is not an automatic process. Rather one has to exhaustively explores very large volumes of data to determine otherwise hidden relationships. The process extracts high quality information that can be used to draw conclusions based on relationships or patterns within the data. However, data mining technique are not easily applicable to Web data due to problems both related with the technology underlying the Web and the lack of standards in the design and implementation of Web pages. Information collected by the Web servers are kept in the server log is the main source of data for analyzing user navigation patterns. Once logs have been pre-processed and sessions have been obtained, there are several kinds of access pattern mining that can be performed depending on the needs of the analyst. Since the method use in this study relied on relatively simple techniques therefore the information gathered is adequate for real user profile data due to the noise in the data has to be first tackled. In this study, Data Mining techniques known as generalized association rules was used in order to get some insights into website usage pattern. For the purpose of this study, server logs from tutor.com portal were retrieved, pre-processed and analyzed. An important finding from this study is that Mathematics subject generally popular from UPSR, PMR and UPSR levels. On the contrary, arts subjects are not popular to Tutor.com users. The system administrator may consider evaluating the content and the link for such subjects, so that the real problem can be identified

Discovering usage patterns from web server logs

As the amount of information available on the World Wide Web (WWW) increases rapidly, the number of sites that hold particular information also increases. In order to have some insights o the site usage, system administrator needs tools that can aid in his usage site’s analysis.To achieve this goal, the use of web mining too is necessary to discover the usage pattern of a particular site. For the purpose of this study, server logs from the educational portal were retrieved, pre-processed and analyzed. Information collected by the Web servers are kept in the server logs and used as the main source of data for analyzing users’ navigation patterns. Once the server logs have been preprocessed and sessions have been obtained, there are several kinds of access pattern mining that can be performed, depending on the needs of the analyst. In this study, data mining technique known as Generalized Association Rule was used in order to get some insights into website usage pattern. The findings from this study provide an overview of the usage pattern of particular educational portal. The study also demonstrates how Generalized Association Rule can be used in site usage analysis. Such a technique enables the discovery of hidden information within the web server logs using data mining technique