Identifying Interesting Knowledge Factors from Big Data for Effective E-Market Prediction

Knowledge management plays an important role in disseminating valuable information. Knowledge creation involves analyzing data and transforming information into knowledge. Knowledge management plays an important role in improving organizational decision-making. It is evident that data mining and predictive analytics contribute a major part in the creation of knowledge and forecast the future outcomes. The ability to predict the performance of the advertising campaigns can become an asset to the advertisers. Tools like Google analytics were able to capture user logs. Large amounts of information ranging from visitor location, visitor flow throughout the website to various actions the visitor performs after clicking an ad resides in those logs. This research approach is an effort to identify key knowledge factors in the marketing sector that can further be optimized for effective e-market prediction

Anonymous web browsing through predicted pages

Anonymous web browsing is an emerging hot topic with many potential applications for privacy and security. However, research on low latency anonymous communication, such as web browsing, is quite limited; one reason is the intolerable delay caused by the current dominant dummy packet padding strategy, as a result, it is hard to satisfy perfect anonymity and limited delay at the same time for web browsing. In this paper, we extend our previous proposal on using prefetched web pages as cover traffic to obtain perfect anonymity for anonymous web browsing, we further explore different aspects in this direction. Based on Shannon&rsquo;s perfect secrecy theory, we formally established a mathematical model for the problem, and defined a metric to measure the cost of achieving perfect anonymity. The experiments on a real world data set demonstrated that the proposed strategy can reduce delay more than ten times compared to the dummy packet padding methods, which confirmed the vast potentials of the proposed strategy.<br /

A Survey of Model Used for Web User’s Browsing Behavior Prediction

The motivation behind the work is that the prediction of web user’s browsing behavior while serving the Internet, reduces the user’s browsing access time and avoids the visit of unnecessary pages to ease network traffic. Various models such as fuzzy interference models, support vector machines (SVMs), artificial neural networks (ANNs), association rule mining (ARM), k-nearest neighbor(kNN) Markov model, Kth order Markov model, all-Kth Markov model and modified Markov model were proposed to handle Web page prediction problem. Many times, the combination of two or more models were used to achieve higher prediction accuracy. This research work introduces the Support Vector Machines for web page prediction. The advantages of using support vector machines is that it offers most robust and accurate classification due to their generalized properties with its solid theoretical foundation and proven effectiveness. Web contains enormous amount of data and web data increases exponentially but the training time for Support vector machine is very large. That is, SVM’s suffer from a widely recognized scalability problem in both memory requirement and computation time when the input dataset is too large. To address this, I aimed at training the Support vector machine model in MapReduce programming model of Hadoop framework, since the MapReduce programming model has the ability to rapidly process large amount of data in parallel. MapReduce works in tandem with Hadoop Distributed File System (HDFS). So proposed approach will solve the scalability problem of present SVM algorithm. Keywords:Web Page Prediction, Support Vector Machines, Hadoop, MapReduce, HDFS

WEB PAGE ACCESS PREDICTION USING FUZZY CLUSTERING BY LOCAL APPROXIMATION MEMBERSHIPS (FLAME) ALGORITHM

ABSTRACT Web page prediction is a technique of web usage mining used to predict the next set of web pages that a user may visit based on the knowledge of previously visited web pages. The World Wide Web (WWW) is a popular and interactive medium for publishing the information. While browsing the web, users are visiting many unwanted pages instead of targeted page. The web usage mining techniques are used to solve that problem by analyzing the web usage patterns for a web site. Clustering is a data mining technique used to identify similar access patterns. If mining is done on those patterns, recommendation accuracy will be improved rather than mining dissimilar access patterns. The discovered patterns can be used for better web page access prediction. Here, two different clustering techniques, namely Fuzzy C-Means (FCM) clustering and FLAME clustering algorithms has been investigated to predict the webpage that will be accessed in the future based on the previous action of browsers behavior. The Performance of FLAME clustering algorithm was found to be better than that of fuzzy C-means, fuzzy K-means algorithms and fuzzy self-organizing maps (SOM). It also improves the user browsing time without compromising prediction accuracy

B.: Predicting www surfing using multiple evidence combination

Abstract The improvement of many applications such as web search, latency reduction, and personalization/ recommendation systems depends on surfing prediction. Predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper, we combine two classification techniques, namely, the Markov model and Support Vector Machines (SVM), to resolve prediction using Dempster&apos;s rule. Such fusion overcomes the inability of the Markov model in predicting the unseen data as well as overcoming the problem of multiclassification in the case of SVM, especially when dealing with large number of classes. We apply feature extraction to increase the power of discrimination of SVM. In addition, during prediction we employ domain knowledge to reduce the number of classifiers for the improvement of accuracy and the reduction of prediction time. We demonstrate the effectiveness of our hybrid approach by comparing our results with widely used techniques, namely, SVM, the Markov model, and association rule mining