Web Usage Mining for UUM Learning Care Using Association Rules

The enormous of information on the World Wide Web makes it obvious candidate for data mining research. Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distint ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E-Learning is one of the Web based application where it will facing with large amount of data. In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. Web usage mining consists of three main phases, namely Data Preprocessing. Pattern Discovering and Patern Analysis. Main resources, server log files become a set of raw data where it's must go through with all the Web usage mining technique, Web usage mining approach has been combined with the basic Association Rules, Apriori Algorithm to optimize the content of the university E-Learning portal. Finally this paper will present an overview of results with the analysis and Web administrator can use the findings for the suitable valuable actions

Web usage mining for UUM learning care using association rules

The enormous content of information on the World Wide Web makes it obvious candidate for data mining research. Application of data milling techniques to the World Wide Web referred as Web mining where this term has been used in three distinct ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E-Leaming is one of the Web based application where it will facing with large amount of data. In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. Web usage mining consists of three main phases, namely Data Preprocessing, Pattern Discovering and Pattern Analysis. Main resources, server log files become a set of raw data where it's must go through with all the Web usage mining phases to produce the final results — set of rides. With the powerful of data mining technique, Web usage mining approach has been combined with the basic Association Rules, Apriori Algorithm to optimize the content of the university E�Learning portal. Finally, this paper will present an overview of results with the analysis and Web administrator can use the findings for the suitable valuable actions

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

Learning lost temporal fuzzy association rules

Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.EPSRC DT

A new approach for web usage mining using case based reasoning

In this study, we present a new approach for Web Usage Mining using Case Based Reasoning. Case-Based Reasoning techniques are a knowledge-based problem-solving approach which is based on the reuse of previous work experience. Thus, the past experience can be deemed as an efficient guide for solving new problems. Web personalization systems which have the capability to adapt the next set of visited pages to individual users according to their interests and navigational behaviors have been proposed. The proposed architecture consists of a number of components, namely, basic log preprocessing, pattern discovery methods (By Case Based Reasoning and peer to peer similarity—Clustering—association rules mining methods), and recommendations. One of the issues considered in this study is that there are no recommendations to those who are different from the existing users in the log file. Also, it is one of the challenges facing the recommendations systems. To deal with this problem, Apriori algorithm was designed individually in order to be utilized in presenting recommendations; in other words, in cases where recommendations may be inadequate, using association rules can enhance the overall system performance recommendations. A new method used in this study is clustering algorithms for Nominal web data. Our evaluations show that the proposed method along with Standard case-classified Log provides more effective recommendations for the users than the Logs with no case classification

M-COMMERCE VS. E-COMMERCE: EXPLORING WEB SESSION BROWSING BEHAVIOR

With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand m-commerce usage behavior. \ \ In this study, we investigate browsing behavior patterns based on the analysis of clickstream data that is recorded in server-side log files. We compare consumers\u27 browsing behaviors in the m-commerce channel against the traditional e-commerce channel. For the comparison, we offer an integrative web usage mining approach, combining visualization graphs, association rules and classification models to analyze the Web server log files of a large Internet retailer in Israel, who introduced m-commerce to its existing e-commerce offerings. \ \ The analysis is expected to reveal typical m-commerce and e-commerce browsing behavior, in terms of session timing and intensity of use and in terms of session navigation patterns. The obtained results will contribute to the emerging research area of m-commerce and can be also used to guide future development of mobile websites and increase their effectiveness. Our preliminary findings are promising. They reveal that browsing behaviors in m-commerce and e-commerce are different

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved