Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Generating dynamic higher-order Markov models in web usage mining

Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter

REVIEW PAPER ON WEB PAGE PREDICTION USING DATA MINING

The continuous growth of the World Wide Web imposes the need of new methods of design and determines how to access a web page in the web usage mining by performing preprocessing of the data in a web page and development of on-line information services. The need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident now a day. Without proper guidance, a visitor often wanders aimlessly without visiting important pages, loses interest, and leaves the site sooner than expected. In proposed system focus on investigating efficient and effective sequential access pattern mining techniques for web usage data. The mined patterns are then used for matching and generating web links for online recommendations. A web page of interest application will be developed for evaluating the quality and effectiveness of the discovered knowledge.   Keyword: Webpage Prediction, Web Mining, MRF, ANN, KNN, GA

Efficient Web Usage Mining Process for Sequential Patterns

The tremendous growth in volume of web usage data results in the boost of web mining research with focus on discovering potentially useful knowledge from web usage data. This paper presents a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access sequences by using a dynamic clustering-based model. It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance. The proposed mining process is verified by experiments with promising results

Bidirectional Growth based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Web sequential patterns are important for analyzing and understanding users behaviour to improve the quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper, we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to effectively prefetch Web pages, thus reducing the users perceived latency. As BGCAP is based on Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10 percent faster for different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates about 5-15 percent more prefetching rules than TD-Mine.Comment: 19 page

Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Improving Web Recommendations Using Web Usage Mining and Web Semantics

This project addresses the topic of improving web recommendations. With the immense increase in the number of websites and web pages on the internet, the issue of suggesting users with the web pages in the area of their interest needs to be addressed as best as possible. Various approaches have been proposed over the years by many researchers and each of them has taken the solution of creating personalized web recommendations a step ahead. Yet, owing to the large possibilities of further improvement, the system proposed in this report takes generating web recommendations one more step ahead. The proposed system uses the information from web usage mining, web semantics and time spent on web pages to improve the recommendations