Analysis of Web Log from Database System utilizing E-web Miner Algorithm

Enormous content of information on the World Wide Web makes it clear for contender for data mining research. Data Mining Technique application is used to the World Wide Web referred as Web mining where this term has been used diverse ways. Web Log Mining is one of the Web based application where it confronts with large amount of log information. In order to produce the web log through portal usage patterns and user behaviors’ recognition, this intended work is an endeavor to apply an efficient web mining algorithm for web log analysis, which is applied to identify the context related with the web design of an e- business web portal that requests security. Because of tremendous utilization of web, web log documents have a tendency to become large resulting in noisy data files. It can find the browsing patterns of client and some class of relationships between the web pages. Here we analyze the logs using web mining algorithm. Whatever the result we will get compare within the Apriori, AprioriAll and E-Web Miner Algorithm. Through the analysis we recognize that web mining algorithm called E-web miner i.e. Efficient Web Mining performs better considering space and time complexity. It can likewise be verified by comparison, candidate sets are much smaller and our results show number of database scanning get reduced due to implementation of E-Web Miner Algorithm. DOI: 10.17762/ijritcc2321-8169.15077

Behavior usage model to manage the best practice of e-learning

This study aims to find e-Learning users’ behavior model that use data mining techniques to predict the successful learning behavior in utilizing e-Learning systems and to develop appropriate e-Learning users’ behavior models that could be used broadly in other higher institutions. Due to the lack of suitable e-Learning user’s behavior model for open source e-Learning system (Moodle) that could not be able to make a prediction for learning outcomes or performances.In this case, it is not useful enough for improving learners’ performance which may cause failure in learning.Therefore, this research is conducted upon three main phases, which are data preparation, data extraction and model verification for generating a verification pattern. This pattern could be used as a direction for creating a more appropriate e-Learning users’ behavior model

Research Proposal on Distinct Study and Significant of Search Techniques in Web Mining

The goal of this research is to provide a more current evaluation and update of web mining research and how machine learning techniques can be applied to web mining techniques available. Currenttrends in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining.Unlike previous investigators, we divide web mining processes into the following five subtasks such as resource finding and retrieving, information selection and preprocessing, patterns analysis and recognition, validation and interpretation, and visualization.Major limitations of web mining research are lack of suitable test collections that can be reused by researchers and difficulty to collect web usage data across different web sites. Most web mining applications have been reviewed in this research. Although the activities are still in their early stages and should continue to develop as the Web evolves. This research shows that frequent pattern growth algorithm produces more efficient and accurate results to compare with K-Apriori algorithm. The proposed methods were successfully tested and results were observed and compared with existing methods on the web log files using machine learning techniques

A Survey on Web Usage Mining, Applications and Tools

World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content.  As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities