Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web
mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified
instances and identify the algorithms that gives better instances accuracy.
Web usage mining technique is widely used for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining can only discover usage pattern explicitly. In order to employ the users’ feature and web pages’ attributes to get more accuracy recommendation, we propose a unified collaborative filtering model for web recommendation which combined the latent and external features of users and web page through back propagation neural networks. In the algorithm, we employ Probabilistic Latent Semantic Analysis (PLSA) method to get latent features. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The preliminary experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.
Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.
Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.
MACHINE LEARNING AND LINK ANALYSIS FOR WEB CONTENT MINING